Ecology

Distribution models were produced for Cuvier’s beaked whale, sperm whale, and Risso’s dolphin based on the availability of both visually and acoustically distinctive features for species detection such as size, body markings and shape, and on temporal-spectral characteristics of their echolocation clicks22,23,24.Visual surveysVisual survey data were collected during five cruises conducted by the National Oceanographic and Atmospheric Administration Southeast Fisheries Science Center (NOAA SEFSC) aboard the R/V Gordon Gunter in 2003, 2004, 2009, 2012, and 2014 (Fig. 1, Supplementary Table 1)25. These cruises were designed to survey the oceanic GOM; therefore, the survey area was delimited by the 200 m bathymetric contour to the north, west, and east, and by the limit of the US exclusive economic zone (EEZ) to the south. Cruises conducted in 2012 and 2014 were limited to the eastern GOM. For this study, cruise data from 2009 was used only for model testing, while other years were used for training. The 2009 data were selected for testing because the entire area was surveyed in that year, allowing model predictions to be evaluated across the full region of interest. Pre-2003 visual survey data were not used due to limited availability of environmental covariate measurements for earlier years.Figure 1Map of GOM visual survey effort for five NOAA cruises between 2003 and 2014 (lines) and passive acoustic monitoring locations (orange triangles). The 2009 cruise effort (red lines) was used for model testing. Track lines for all other years, used for model training, are shown in black. The gray outline shows the extent of the modeled region, a pelagic area encompassing depths greater than 200 m within the US EEZ. Bathymetric contours (blue lines) are shown for the 200 m, 1000 m and 2000 m contours (Map created using ArcGIS software by ESRI29).Full size imageVisual survey effort was conducted along transect lines with the vessel traveling at or above 18.5 km/h (10 kn). To mitigate spatial autocorrelation between successive sightings, transect lines were divided into equal length segments of 10 km or less with transect segments each representing approximately 0.5 h of survey effort. The visual survey dataset consisted of 1,956 training segments and 449 test segments. Observations were used as point estimates. Implications of this approach are considered in the discussion.On all visual surveys, observation data were collected by one team of trained visual observers on the vessel’s flying bridge using 150 × 25 Bigeye binoculars to search for, identify, and estimate group sizes of cetaceans. All surveys operated in closing mode with the vessel departing from the track line for closer approaches to identify to the lowest possible taxonomic level and to obtain group size estimates.Raw count data were converted into densities for each 10 km transect segment for each of the three species of interest. All sightings for each of the species of interest are shown in Supplementary Figs. 1, 3, and 5. To obtain densities using distance sampling methodologies, the best model to fit the distribution of sighting distances was selected from a range of options (half normal, hazard-rate, hazard-rate with a second order polynomial adjustment, or uniform) using AIC implemented in the R software package mrds26. The species-specific sighting probability (Pvis) along a transect segment was given by the fitted detection function. Species-specific estimated truncation distances (or effective strip half width; w) were computed as the distance from the transect line within which 95% of the sightings of each species occurred (Supplementary Figs. 2, 4, and 6).For each transect segment and species, the total area monitored visually (({A}_{Vis})) was computed as$${A}_{Vis} = 2wL$$
(1)
where L is the transect segment length. Animal density was calculated for all transect segments as the number of animals detected per 1000 km2.Density (({widehat{D}}_{t}^{V})) along each visual survey transect segment t was calculated as.$$hat{D}_{t}^{V} = hat{G}_{tot} /({text{A}}_{vis} cdot , gleft( 0 right) , cdothat{P}_{vis} )$$
(2)
where (widehat{G}) tot is the sum of the best estimate group sizes from all sightings of the species of interest along the transect segment, and g(0) is the probability of observing the species directly on the transect line27 (Supplementary Table 2). Estimation of g(0) typically requires survey effort using independent (double-blind) observer teams and estimates were not available for the GOM surveys; therefore g(0) was estimated for each species from western Atlantic surveys aboard a similarly-sized vessel (R/V Endeavor), as the average of g(0) estimates from upper and lower observation teams28. The upper and lower observation platforms of the R/V Endeavor were 17.6 and 10.2 m high respectively and a cruise speed of 10 knots. The R/V Gordon Gunter has a primary observation deck height of 13.9 m, and a survey speed of 10 knots.Passive acoustic monitoringPAM data were collected from five sites in the GOM (Fig. 1) between 2011 and 2013 (Supplementary Table 2) using High-frequency Acoustic Recording Packages (HARPs)30. Recordings from three deep ( > 1000 m bottom depth) monitoring sites were used for this study’s deepest-diving species, sperm whales and Cuvier’s beaked whales. Recordings from two additional continental shelf monitoring sites ( More

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Based on the composition of the dated samples, two calibration procedures must be undertaken to transform the radiocarbon (14C) dates into accurate calendar dates. The 14C dates of the wood, newspaper and textile fragments were calibrated using the IntCal20 atmospheric calibration curve22 (Table 2, Fig. 4). All results are statistically consistent and give calibrated dates between 1646 and 1950 AD. The combination of the three dates provides the interval 1667–1950 AD. The elongated distribution is due to the flat shape of the calibration curve for this period23. Nevertheless, the results show that all the wood, newspaper and textile samples found inside the statue definitively date after 1650.Figure 4Calibrated 14C dates for wood, textile and paper samples taken from the Flora bust (in grey). The statistical combination of the three dates in green gives the interval of 1667–1950 AD. The χ2 test value of T = 4.1 (5% 6.0) shows their consistency.Full size imageTo calibrate the 14C dates obtained from the wax samples, the composition of the material has to be carefully considered. The Flora bust and “Leda and the swan” relief waxes are principally composed of spermaceti from a sperm whale that lives in the ocean, mixed with minor amounts of beeswax and other organic compounds extracted from terrestrial animals. The wax is thus primarily composed of marine material with some of terrestrial origin. The 14C source of terrestrial animals is in equilibrium with the atmosphere whereas that of whales 14C source is subject to the Marine Reservoir Effect (MRE)24. The MRE affects 14C dates since carbon consumed by organisms in the ocean is older than that consumed on land. Because the wax used for the sculptures is composed of carbon from different sources, other than just atmospheric carbon, the 14C measurements produce apparent old uncalibrated radiocarbon ages from 340 to 420 BP (Table 3) and a correction is needed to compensate this effect in calibration calculations.The mixture of marine and terrestrial sources in the wax requires the use of a combination of two calibration curves: IntCal20 atmospheric22 and Marine20 marine25, both weighted by the proportion of terrestrial and marine materials. In the case of the Flora bust, the determination of the exact ratio of spermaceti wax and terrestrial wax was not feasible because only a few samples of wax were available for analysis.To further complicate the procedure, the location of the marine source must be known to accurately calibrate marine material. Whales travel long distances, integrating the reservoir ages of the different water masses along their paths making that the determination of the marine reservoir age (MRA) for whale material 14C dates difficult. The global-average (MRA) of surface waters is c. 500 years25 but values range from about 400 years in subtropical oceans to over 1000 years in the poles. According to our knowledge no MRA has been reported for sperm whale (Physeter Macrocephalus L.) bone or for spermaceti except the estimation of 300 ± 200 years made by Freundlich5. Various values can be found for other cetacean materials in literature. One of the more complete studies, which is based on the analysis of 21 whales caught in Norway during the 19th c., proposed an average marine reservoir age (MRA) of 370 ± 30 years for various whales from the North Atlantic26. Previous publications recommended to use a c. 200 years marine reservoir correction for bowhead whales from Canadian Artic27, or determined a mean value correction of 320 ± 35 years for marine mammals, including whales, living near Sweden28 or c. 350 years correction for a 17th c. Finnback whale bone collected in Spitsbergen29. Additionally, based on an exhaustive compilation of published marine mammal radiocarbon dates, both live-harvested materials and subfossils, from the Canadian Arctic Archipelago, Furze et al.30 provided reservoir offset values for beluga (D. leucas) and bowhead (B. mysticetus) corresponding to a MRA of 570 ± 95 years for the latter.Calibration of the 14C dates of the 19th c. wax objects made by Richard Cockle LucasSince the spermaceti MRA value and the spermaceti wax content cannot be determined precisely, another approach was developed to calibrate the 14C dates of the Flora bust. This approach is based on the well-dated wax relief, “Leda and the Swan”. This relief was created by R. C. Lucas in 1850 and the chemical analysis has shown that its composition is similar to that of the Flora bust (Figs. 2, 3). The “Leda and the Swan” relief was used as reference to determine the appropriate combination of the IntCal20 and Marine20 calibration curves to be applied to the Flora wax material. The percentage of each curve was established by adjusting the calibrated date distribution of the Leda relief on both sides of the year 1850. To obtain this result, a combination of 15% atmospheric/85% marine curves was selected with an uncertainty of 10% to reflect material variability. The resulting distribution of dates is from 1704 to 1950 AD (Table 3, lower part of Fig. 5) which is not very precise, but this method has the advantage to take into account uncertainties on spermaceti MRA and on the spermaceti/beeswax content ratio. Figure 5 also shows that the results calibrated with the IntCal20 atmospheric curve are inconsistent with the known date of creation of the “Leda and the Swan” relief, which confirms the presence of marine material in the wax.Figure 5Calibrated 14C dates for the wax samples of the Leda and the Swan relief using atmospheric curve only, in light grey and light green, give dates out of range of the known date of creation of this artwork made by Lucas in 1850. A calibration of the same samples with a combination of 15% atmospheric/85% marine (± 10%) calibration curves, in dark grey, gives dates in the time frame of the relief’s creation in 1850. The statistical combination of the three dates, in blue, gives the interval of 1704–1950 AD.Full size imageCalibration of the 14C dates of the Flora bustThe same combination of atmospheric and marine calibration curves was applied to calibrate the 14C dates obtained for wax samples taken from six different locations at the surface and inside of the Flora bust because the composition of the Flora is similar to that of the Lucas wax objects. The results are presented in Fig. 6 and Table 3. All the dates are after 1704 AD, with a statistical combination on the six dates of 1712–1950. Uncertainty on the calibration curves lead to a broad interval for the dates of the Flora wax with about two centuries precision. Calibrated dates obtained on the wax samples, when the MRE is taken into account, agree with those of the wood, paper and textile samples, which confirms the strength and validity of our approach. All of the analysed constituents of the Flora bust are dated after 1700 AD, precluding the bust from being created in the Renaissance period.Figure 6Calibrated 14C dates for the wax samples of the Flora bust using a combination of 15% atmospheric/85% marine (± 10%) calibration curves (in dark grey). The statistical combination of the three dates in blue gives the interval of 1707–1950 AD.Full size imageChemical analyses and absolute dating were performed on different materials and several wax samples taken from the surface and inner parts from the Flora bust as well as on two dated wax reliefs made by the British 19th c. sculptor Richard Cockle Lucas, who some claim is the author of the Flora bust. The Lucas object “Leda and the swan” dated at 1850 could only be accurately dated using 14C measurements when a mixed terrestrial and marine calibration was taken into consideration because the wax is primarily made from spermaceti with minor amount of beeswax. Because the spermaceti was extracted from sperm whales living in deep and shallow seawaters, 14C dating must to consider the MRE. The Flora bust was shown to have an extremely similar composition to the Lucas object. Thus the same calibration correction procedure was applied to the uncalibrated 14C dates of the Flora bust. This new procedure involved calibrating of the 14C dates by considering a combination of 85% marine/15% atmospheric curves. The result dates the Flora materials to the 18-19th c., which proves that the bust was not produced during the Renaissance, and therefore cannot be attributed to Leonardo. This study also illustrates that 14C dating must take into account the heterogeneity and diversity of art objects, some of which may contain uncommon materials such as spermaceti wax.While it is somewhat disappointing to learn that the bust cannot be attributed to Leonardo, this information does provide useful insight into history. The sperm whale population suffered a serious decline in the 1740s when sperm whaling started on an industrial scale. The use of spermaceti in art objects shows how widespread the use of sperm whale products was and highlights the whaling industry’s importance during the industrial revolution. Other culturally significant objects may also be composed of materials that show the importance of certain industries or materials. There is clearly a need for art historical research to integrate natural science investigations in order to provide information allowing an improved attribution of art works and allowing to give another dimension to the historical value of such objects. More

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Fossils, such as this skeleton of a T. rex on display in the Netherlands, may be even rarer than scientists realised. Credit: Marten van Dijl/AFP via Getty

Ever wondered how many Tyrannosaurus rex ever roamed the Earth? The answer is 2.5 billion over the two million or so years for which the species existed, according to a calculation published today in Science1. The figure has allowed researchers to estimate just how exceedingly rare it is for animals to fossilize.Palaeontologists led by Charles Marshall at the University of California, Berkeley, used a method employed by ecologists studying contemporary creatures to estimate the population density of T. rex during the late Cretaceous period. “You hold a fossil in your hand and you know it’s rare. The question is, how rare?” says Marshall. “To know that, you need to know how many of them existed.”To do that, he and his co-authors turned to a method used to estimate the population density of living animals from their body mass and the geographic ranges that they occupy. Damuth’s Law stipulates that the average population density of a species decreases in a predictable way as body mass increases; for example, there are fewer elephants than mice in a given area.Chances of being fossilized vanishingly smallThe team used their estimates of the total range of T. rex across modern North America, combined with their estimates of the dinosaur’s body mass, to calculate that, at any one time, around 20,000 T. rex would have been alive on the planet. That translates to around 3,800 T. rex in an area the size of California, or just two T. rex patrolling Washington DC. Calculating that T. rex survived for about 127,000 generations before becoming extinct, the researchers came up with a figure of 2.5 billion individuals over the species’ entire existence. Only 32 adult T. rex have been discovered as fossils, so the fossil record accounts for just 1 in about every 80 million T. rex. This means that the chances of being fossilized — even for one of the largest-ever carnivores — were vanishingly small.These numbers suggest that fossils in general are exceedingly rare, and hint that many species that were much less widespread than T. rex were probably never preserved, says Marshall, who adds: “The fossil record is our only direct knowledge of these completely unimaginable past histories of our planet.”Thomas Holtz, a vertebrate palaeontologist at the University of Maryland in College Park, calls the calculation an “interesting speculation”, adding that “we always knew that the chance of any individual becoming a fossil was exceedingly rare, but we lacked the calculation to figure out how rare”.But he says it would be good “to see someone ground-truth these kinds of estimations against living species to get a better sense of accuracy”. He’d also like to see comparable studies made on extinct species with more abundant fossils, such as woolly mammoths, Neanderthals and dire wolves, which might allow us to better understand historic ecosystems. More

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The BNNs demonstrated that the average yields of cacao farmer groups, in Sulawesi over distinct time periods, are closely associated with the ENSO OI patterns 9 to 25 months before harvest. The ENSO OI short term pattern explained slightly less (69%) of the variation in the average yield than the long term pattern (77%). We consider both these levels of prediction to be high, however, the short term pattern level was simpler and was used for further analyis. The linear regression between predicted and actual yields indicates that the model will tend to underestimate cacao productivity at high yields (e.g. in excess of 100 kg ha−1 month−1).The predictions made by the BNNs indicated that cacao yields are substantially impacted by ENSO conditions, which accords with prior observations21. The fertilizer response varied according to the ENSO profile: the greatest predicted response was in the Neutral ENSO profile with a smaller response under the MinCent ENSO profile, especially when unfertilized yields were low, and essentially no response under the MaxCent ENSO profile. Hence, the analysis provides insights into the appropriate fertilizer regime for distinct ENSO OI patterns in the period 9 months before harvest. We also note that recent methods to improve prediction of future ENSO OI patterns make it possible to predict them with reasonable accuracy for up to 1 year3. Thus, it is possible to relate average cacao crop performance and management practices directly to ENSO patterns in a given region without the need for weather data when the following conditions are met: (1) data exist on crop performance in any given site over time with distinct management practices; and (2) the weather patterns are driven by ENSO OI. We have used cacao as proof-of-principle, and suggest that this principle can readily be applied to other crops.A great advantage that Bayesian methods have over other machine learning approaches is that they can utilise variance based probability distributions to predict the likelihood of any given outcome. The model was used to predict the most likely monthly yield and expected standard deviation from each farm group under a specific ENSO profile when either fertilized or unfertilized. The standard deviations attained across all predicted responses was remarkably low, typically less than 1 kg ha−1 per month. Both the construction of the model and the subsequent predictions were based upon the mean yield data from 10 farms in each group at each monthly harvest under a single management type. As a result, all variations in yield across those 10 farms would have been excluded from the network constructed. As a consequence, while the predictions returned by the model might precisely reflect the mean response from each group, the limited input data will mean that the range of possible outcomes under any predicted scenario is likely to be underestimated. Up to now we have established proof-of-principle stage, the next stage will be first to improve the assessment of the predicted probability distributions and then to develop channels for communicating the results of the analysis to farmers followed by appraisal of their opinions and use of the information provided. Options for improving estimates of the probability distribution include both incorporating all observations from within each group, to ensure that farm-to-farm variance is adequately captured, and to extend the observations across more seasons to ensure that the variability of response to contrasting ENSO profiles is better represented.The analysis presented here is based on the average yields for each group of farmers. However, previous analysis indicates much variation in yield within the farmers groups20. Furthermore, those farmers with higher average yields tended to maintain their yield advantage relative to those with lower yields, even when conditions were adverse. This supports the view that the differences in yield between the high average yield and the low average yield farmers are due to management skills, rather than more favorable soils and weather conditions20. This suggests that if the average yields of individual farmers relative to the mean of all farmers are known, then the ENSO predictions can be used to predict their yield levels, and also their response to fertilizer applications.The demonstration that on farm yields and response to one management variable, fertilizer, can be linked directly to ENSO OI data supports the view that, in the future, with cacao or other crops, data on farm yields obtained with distinct management practices can be coupled with ENSO OI data to both determine probable crop yields and also to define differential crop response to management at specific sites under distinct ENSO OI patterns without the need for accurate weather data. The ENSO OI data exists, what is often lacking is data on yield with distinct management practices. To obtain this type of information in heterogeneous growing environments using traditional Randomized Control Trials is simply not possible. However, we suggest that schemes, such as those to collect the cacao data we have here with distinct management treatments superimposed on farmers fields20, can be used. Furthermore, even without superimposing management practices, simply monitoring crop performance, weather and the variation in management practices of farmers can be used to relate yield to variation in weather patterns and management28,29,30. However, this is only effective if the data of a large number of cropping events is brought together for analysis, which requires social organization and the willingness to share data28. Our experience with cacao indicates that small farmers are willing to share data, but an external agency is required to manage the overall process of data collection and compilation20. Similar experiences with CropCheck and in Australia and Chile support this point of view31,32. The value of shared information through formation of farmer groups is well established33,34 and we suggest that the methodology described here could be implemented through farmer groups. Hence, through monitoring of crop performance and management coupled with Bayesian based machine learning tools and currently available ENSO OI information and predictions, farmers and agronomists can adjust management practices, in this case fertilizer applications, according to ENSO profiles. This will require social organization and support for the collection, compilation and analysis of the data; however, we believe it offers a route to provide farmers with an improved and cost effective knowledge base, derived from sparse data resources, to better manage their crops.Social organization is not only required for the collection of data to be analysed, but also for the disemination to farmers of the knowledge generated though its interpretation. Current tendencies of providing farmers with the basis to make better decisions recognise the restrictions of the linear model for extension and tend towards active farmer participation in the interpretation of data through such mechanisms as farmers field schools35, formation of farmers groups (see for example Montaner 200434) and innnovation networks (see for example Klerkx et al. 201036, Wood et al. 201437, World Bank, 200838). Further development of farmers´organizations and innovation networks will be required to effectively deploy the concepts presented in this paper.The principles developed here could be applied to other crops, such as coffee, olive and oil palm, and this type of analysis could be extended to other regions, such as Africa where data on crop response to management and weather variation is sparse. At the same time, we note that additional information on, inter alia, crop management, topography and soil types could substantially improve the predictive power of the networks. Furthermore, these machine learning techniques can be used to mine existing big data sets collected by large commercial interests, to discover relationships between environment, management and crop production, and thereby supplement, at low cost, the findings generated by formal controlled scientific experiments. In the case of small farmers, social organization and external support will be required.There are several caveats on the use of this proposed methodology. First, the relationship between the ENSO phenomenon and the weather patterns will be specific to each location or recommendation domain. Hence, models and inferences for management cannot be readily transferred from one recommendation domain to another. Furthermore, the definition of the area that comprises a recommendation domain is not simple. Thus, whilst we consider the principles developed here to be universal, the models themselves will be specific to each recommendation domain, which are currently still difficult to define but new approaches are becoming increasingly available to do so (e.g. Rubiano et. al. 201618; Rattalino Edreira et al. 201817).A further complication of the suggested approach is the lack of understanding of the underlying mechanisms that establish the associations. This deficiency limits the ability to identify the specific causes of different crop productivities, and thus limits our ability to resolve these unidentified problems.Growers decisions on how much to invest in their crop production practices depends on the expected prices of the commodities they produce: when prices are expected to be high, they will invest more, and when prices are low they may even abandon their crops. It has not escaped our notice that the predictive power of the machine learning resources would also provide the cacao industry as a whole with insights into the fluctuations in future cacao supply and hence prices. This would allow farmers and others in the cacao supply chain to minimize uncertainty and better manage the overall industry. The experiences strongly support the idea that machine learning is a useful tool in our armoury opening the opportunity to utilize information from on farm performance coupled with publicly available data to improve agricultural management. More

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