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    Drivers of habitat quality for a reintroduced elk herd

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    Revealing the global longline fleet with satellite radar

    To estimate the total number of non-broadcasting vessels, including those that were not detected by SAR, we: (1) obtained SAR detections of vessels from RADARSAT-2 and the corresponding vessel lengths as estimated from the SAR image; (2) processed a global feed of AIS data to identify every broadcasting vessel that should have appeared in the SAR images at the moment the images were taken; (3) developed a novel technique to determine which vessels in AIS matched to detections in SAR, which AIS vessels were not detected by SAR, and which SAR detections represented non-broadcasting vessels; (4) after matching SAR to AIS, we could then (a) model the relationship between a vessel’s actual length and the length as estimated by the SAR image (Fig. 3b) and (b) model the relationship between the likelihood that a vessel is detected and its length (Fig. 3a); and (5) finally, we combined these relationships to develop an estimate of the number and lengths of non-broadcasting vessels in the region.SAR imagery and vessel detectionsWorking with the satellite company Kongsberg Satellite Services (KSAT), we tasked the Canadian Space Agency’s satellite RADARSAT-2 to acquire SAR images from its ship detection mode (DVWF mode, GRD product), with a pixel size of about 40 m and a swath width over 400 km (19). These images were processed following standard procedures for GRD products (e.g. applying radiometric calibration and geometric corrections)29,30. Vessel locations were extracted from the images with the widely used ship detection algorithms, which discriminates objects at sea based on the backscatter difference (pixel values) between the sea clutter and the targets31. Vessel lengths were estimated by measuring distances directly on the images with the aid of a graphical user interface tool31.Identifying Vessels using AISIn each region, AIS data, obtained from satellite providers ORBCOMM and Spire, were processed using Global Fishing Watch’s data pipeline1. The identities and lengths of all AIS devices that operated near the SAR scenes in both space and time were first obtained using Global Fishing Watch’s database1. To be sure vessels were identified correctly, two analysts reviewed the tracks of every AIS device in each region.In both regions, it is common practice for fishers to put AIS beacons on their longlines, likely to aid in retrieving them, meaning that many AIS devices were longline gear and not vessels. Because gear outnumbered vessels by several-fold, it was critical to differentiate gear and fishing vessels. In the Indian Ocean, 521 unique AIS devices associated with gear were detected that were likely within the SAR scenes, and 390 unique AIS devices associated with gear in the Pacific that were likely within the SAR scenes. Transponders were determined to be associated with gear by inspecting the name broadcast in the AIS messages (gear frequently broadcasts one of several standard names and/or a voltage reading) and classification using the Global Fishing Watch vessel classification algorithm1. Most gear also had an MMSI number (unique identifier number for AIS) that started with 1, 8, or 9 or broadcast names that signified gear. We eliminated all gear from the analysis because (1) these gear buoys have reflectors that are only ~ 1 m in size, and they should not be visible in ~ 40 m resolution SAR images, and (2) we found that gear matched to SAR detections only when traveling faster than 2 knots (and thus was on the deck of a boat); of 159 instances of gear in scenes where the gear was traveling slower than two knots, zero matched to a radar detection (Fig. S9).Generating probability rasters for matching AIS to SARMost AIS positions did not correspond to the exact time when the SAR images were taken. Hence, to determine the likelihood that a vessel broadcasting AIS corresponded to a specific SAR detection, we first developed probability rasters of where a vessel was likely to be minutes before or after a GPS position was recorded (Figs. S1,S2). We mined one year of global AIS data, including roughly 10 billion GPS positions, and computed these rasters for six different vessel classes (trawlers, purse seines, tug, cargo or tanker, drifting longlines, and others) and considered six different speeds (1, 3, 5, 7, 9, and 12.5 knots) and 36 time intervals (− 448, − 320, − 224, − 160, − 112, − 80, − 56, − 40, − 28, − 20, − 14, − 10, − 7, − 5, − 3.5, − 2.5, − 1.5, − 0.5, 0.5, 1.5, 2.5, 3.5, 5, 7, 10, 14, 20, 28, 40, 56, 80, 112, 160, 224, 320, and 448 min).For example, we queried a year of AIS data to find every example of where a tugboat had two positions that were 10 min apart from one another when the vessel had been traveling at 10 knots at the first position. We then recorded each of these locations relative to the location the vessel would have been if it traveled in a straight line, with x coordinates being in the direction of travel and the y coordinates being perpendicular to the direction of travel. When collected for hundreds of thousands of examples across the AIS dataset, the result is a heatmap of where tug boats are located 10 min after a position when it was traveling at 10 knots. The raster is centered on a point that is the extrapolated position of the vessel based on its speed. For instance, the purse seine raster that corresponds to a vessel traveling between 6 and 8 knots between 96 and 128 min after the most recent position is centered at a point that is 13.1 km (7 knots × 112 min) straight ahead of the direction the vessel was traveling. Figure S1 shows samples of these rasters for different vessels.We built rasters of 1000 by 1000 pixels for each vessel class and time interval, with the area covered by the raster dependent on the time interval (longer time intervals imply longer traveled distances, covering more area). The scale of each pixel was given by:$${text{pixel}};{text{width = max(1, }}Delta {text{m) / 1000}}$$
    (1)
    where Δm is the time interval in minutes, and pixel width is measured in km. Thus, if the Δm is under one minute, the entire raster is one kilometer wide with each pixel one meter by one meter. If the time is 10 min, then each pixel is 10 m wide, and the entire raster is 10 km by 10 km.Since the pixel width varies between rasters, the units of the rasters are probability per km2, thus summing the area of each pixel times its value equals one. Six vessel classes with 36 time intervals for each and six speeds led to 1296 different rasters. This probability raster approach could be seen as a utilization distribution32—for each vessel class, speed and time interval—where the space is relative to the position of the individual.Combining probability rasters to produce a matching scoreFor a few vessels (~ 4%) there was only one AIS position available before or after the scene. This resulted from a long gap in the AIS data due to poor reception, a weak AIS device, or cases where the vessels disabled their AIS. For these vessels, we used the raster values for a single position. For the vast majority of vessels, however, there was a GPS position right before and after the scene, and thus two probability rasters. We used two methods to combine these probability rasters to obtain information about the most likely location:Multiply and renormalize the rastersTo multiply the rasters, we interpolated the raster values, using bilinear interpolation, to a constant grid at the highest resolution between the before and after rasters. Then, we multiplied the values at each point and renormalized the resulting raster (Fig. S2):$$p_{i} = frac{{p_{ai} cdot p_{bi} }}{{mathop sum nolimits_{k = 0}^{N} p_{ak} cdot p_{bk} cdot da}}$$
    (2)
    where pi is the probability in vessel density per km2 at location i, pai is the value of the raster before the image, pbi is the value of the raster after the image. The denominator is the sum of all multiplied values across the raster, scaled by the area of each cell, da.Weight and average the rasters For this method, we weighted the raster by the squared value of the probabilities of that scene. This has the effect of giving the concentrated raster a higher weight, thus weighting higher the raster that is closer in time to the image:$${w}_{a}=sum_{k=0}^{N} {p}_{ak}^{2}cdot da$$
    (3)
    and the weighted average at location i is:$${p}_{i}=frac{{p}_{ai}cdot {w}_{a}+{p}_{bi}cdot {w}_{b}}{{w}_{a}+{w}_{b}}$$
    (4)
    where wa is the weight for raster a, wb the weight for raster b (calculation analogous to wa’s in Eq. 3), pi is the probability in vessel density per km2 at location i.To determine whether we should multiply (Eq. 2) or average (Eq. 4) the probabilities, we compared the performance of these two metrics against a direct inspection of the detections. We found that at short intervals, multiplying the rasters and renormalizing often made probability values extremely small ( {d}_{d}cdot {p}_{d} + {p}_{f}$$
    (5)
    where ({p}_{v}) is the probability density of the vessel presence at the location of the SAR detection (the score listed above), ({p}_{d}) is the probability that the vessel is detected by SAR, ({d}_{d}) is the density of non-broadcasting vessels in the region, and ({p}_{f}) is the density of false detections in the scene. The greater ({p}_{d}), the more dark vessels there are in a scene, and the more likely it is that any given detection is a dark vessel instead of a vessel broadcasting AIS. The right-hand side of the equation ({d}_{d}cdot {p}_{d} + {p}_{f}) should roughly equal the number of detections per unit area that do not match to AIS in the region. In other words, the probability of the vessel with AIS being at that specific location and detected by SAR (left side of the equation) should be greater than the probability of a dark vessel or a false detection at that location (right side of the equation).The total number of unmatched vessels in each studied region normalized by total area covered gives a density of non-broadcasting vessels of 2.6–2.8 × 10–5 vessels km-2 (Indian Ocean) and 6.8–7.2 × 10–6 vessels km−2 (Pacific Ocean), similar to the thresholds estimated by analysts. For the most likely number of matched vessels, we use a threshold that is halfway between the higher and lower bound of the analyst (5 × 10–5 to 1 × 10–4), 2.5 × 10–5 which is also roughly equal to the theoretical estimate of the Indian Ocean.This threshold approach performed significantly better than a metric based on the distance between the SAR detection and the most likely location of the vessel, where the likely location is based on extrapolating speed and course of the position closest in time to the image (Fig. S4).Determining whether a vessel with AIS was within a sceneVessel positions from AIS are usually available before and/or after the SAR images, and sometimes it is unclear if a vessel should have been within the scene footprint at the time of the image.To estimate the probability that a vessel (with AIS) was within a scene, we used the multiplied probability raster, summing the values inside the scene boundaries. This provides an estimate of the likelihood that the vessel was within the scene footprint at the time of the image. We applied this to every vessel that had at least one AIS position within 12 h and 200 nautical miles of the scene footprint. The vast majority of vessels were either very likely inside or outside the scene footprints, with 516 vessels having a probability of  > 95% and only 16 having a probability between 5 and 95%. We filtered out all vessels that were definitely outside of the image footprint before matching.Estimating the likelihood of detecting a vessel with SARThe AIS data show that not all vessels broadcasting AIS were captured by the RADARSAT-2 images (Fig. 3a). Using the known lengths of detected vessels with AIS, we estimated the likelihood of detecting a vessel with SAR as a function of vessel length (Fig. 3a). For vessels shorter than 60 m, we approximated the detection rate as a linear function. Treating each vessel as an individual detection, we fitted the 50th percentile using quantile regression to approximate the detection rate. For vessels above 60 m, we assumed a constant detection rate as very few vessels above this length did now show up in the SAR images. Of the 46 unique vessels larger than 62 m, 42 were detected, implying a detection rate of ~ 91%. Given that it is highly likely that large vessels will be captured by medium-resolution SAR imagery, we manually reviewed these cases to confirm that they were (almost surely) inside the scene footprints at the time the images were taken.We should note that the probability of detecting a vessel in SAR also depends on the sea state, incidence angle, polarization, material of the vessel, and orientation of the vessel. We are unable, however, to measure these effects directly so we cannot explicitly model these effects.With sufficient scenes, these effects should be randomly distributed across our scenes, so they likely account for some of the variability in detectability and the inaccuracy in our length estimates from SAR.Estimating the number and length of non-broadcasting vesselsBecause SAR does not detect all vessels, and because the length as estimated by SAR can be incorrect, there are many possible distributions of actual non-broadcasting vessels that could have produced the distribution of unmatched SAR detections that we found in the scenes. To estimate the most likely such distribution, we built a model to combine the two key relationships—between vessel length and likelihood of detection, and between vessel length and the length as estimated by SAR. This model allowed us to estimate, based on the number and distribution of SAR vessels, the likely number and distribution of actual vessels present (Fig. 3c,d).We binned the likelihood of vessel detection as a function of length into 1 m intervals, yielding a vector (alpha) of length 400. We also binned into 1 m intervals the population of lengths of all detected vessels ((ell_{D})) as reported by AIS (i.e. number of vessels at each length bin), the population of expected SAR lengths ((ell_{E})), and the population of lengths of all vessels ((ell_{A}), the quantity we wish to estimate). Thus, (ell_{D}) can be expressed as the product of (alpha) and (ell_{A}):$$ell_{D} = {upalpha } odot ell_{{text{A}}}$$
    (6)
    where (odot) is the element-wise product. We then estimated a matrix (L_{{}}) that relates (ell_{D}) to (ell_{E}).$$ell_{E} = Lell_{D}$$
    (7)
    where each element (L_{ij}) represents the probability that a vessel with length in bin j would be estimated by SAR to be of length in bin i. We calculated these probabilities as lognormal probability density functions, with one distribution per column. To estimate the scale and shape parameters of these distributions, we first fitted a quantile regression using the (non-binned) lengths from AIS of detected vessels as the predictor for the lengths reported by SAR. Assuming that the predicted 1/3 and 2/3 quantiles (as shown in Fig. 3a) represent the quantiles of a lognormal distribution, allow us to calculate the shape and scale parameters. We chose a lognormal distribution because: 1) the variable of interest, length, was always greater than zero, 2) the population of lengths was skewed towards larger values, and 3) there is an explicit and relatively simple relationship between the lognormal quantiles and the shape and scale parameters that simplified the calculations.Combining Eqs. (6) and (7) provides a relation between (ell_{A}) and (ell_{E}):$$ell_{E} = {text{L}}left( {alpha odot ell_{A} } right)$$
    (8)
    To estimate ({mathcal{l}}_{A}) we minimized an objective function (O({mathcal{l}}_{E},{mathcal{l}}_{o})) between the vector of expected counts binned by length (({mathcal{l}}_{E})) and the vector of counts observed in SAR binned by length (({mathcal{l}}_{o})). For this objective function, we chose the sum of the Kolmogorov –Smirnov distance between length distributions and the squared difference of the total numbers of detections. The first term controls the shape of the resulting distribution while the second one controls the magnitude. Specifically:$$Oleft( {ell_{E} ,ell_{o} } right) = max left( {left| {C_{E} – C_{O} } right|} right) + left( {T_{E} – T_{O} } right)^{2}$$
    (9)
    where:$$T_{x} = mathop sum limits_{ } ell_{x}$$$$D_{x} = ell_{x} /T_{x}$$$$C_{x} = cumsumleft( {D_{x} } right)$$Assessing the uncertainty in the estimationTo test how accurately our approach predicts the correct number of vessels, we performed a bootstrap simulation. We computed the vector (alpha) and the matrix L from a random subset of vessels with AIS that had a high confidence ( > 95%) of appearing within the scenes. We then used our method on the SAR detections that matched the remaining vessels to predict the number of vessels they corresponded to ((ell_{text{A}})). By running 10,000 experiments we found a mean absolute percent error of + − 9% (Figs. S5 and S6). This provides a rough estimate of the uncertainty in our prediction due to the estimation process itself. We used the distribution of these samples to estimate the 90% confidence interval that we report with our estimates. We note that this uncertainty refers to the parametrization of the model and there may be other sources of error, such as the possibility that vessels without AIS have different radar properties (e.g. made out of materials with different reflectiveness), that we did not account for in our model.Catch and effort data in the overlapping area between WCPFC and IATTCWe downloaded gridded effort and catch data from the WCPFC and IATTC websites, and compared the reported number of hooks and catch from September to December of 2019 for the area between − 140 to − 150 longitude and − 5 to − 15 latitude, a bounding box that contains our study region in the Pacific and which is entirely within both the WCPFC and IATTC convention zones. We found that the reported number of hooks for Korea is three times higher for the IATTC as it is for the WCPFC (Fig. S7), and the numbers of hooks also disagree by more than 10% for most other flag states. Catch is also 2.5 times higher for IATTC than for WCPFC for Korea as well, with catch also differing by more than 10% for most other flag states. This finding suggests that the different RFMOs may not be accounting for the same vessels in the overlap region between the two RFMOs. More

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    Sap flow of sweet cherry reveals distinct effects of humidity and wind under rain covered and netted protected cropping systems

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    A survey of vocal mimicry in companion parrots

    It is well known that parrots are excellent vocal learners; here we quantified that ability across a wide variety of species, using human mimicry as a proxy for vocal learning of natural repertoires. Results confirm that parrot vocal mimicry varies substantially both within and among species22. Parrot age, social interactions, and sex do not appear to be universal drivers of vocal learning ability within the order Psittaciformes, but all of these factors may have effects within individual species.Vocal learning variation by speciesWithin species, mimicry sound repertoires are extremely variable bird to bird; for example, our data indicate that a grey parrot may mimic anywhere from 0 to 600 different human words. Many other species showed smaller repertoires but similar variability. It is not entirely clear whether this range of variation would be present in natural sounds within wild parrot populations, but research has demonstrated intraspecific repertoire size variation in multiple species of parrots30,31.The vast majority of parrots presented a pattern in which their repertoire size was largest for words, intermediate for phrases (composed of the reported words), and smallest for non-linguistic sounds (Fig. 2). In the wild, parrots mimic the most socially relevant vocalizations, and presumably do so in captivity as well15. Thus, the spoken word and phrase interactions with their human “flock” likely reflect the most socially relevant cues. The interesting exceptions to this pattern were Fischer’s lovebirds, cockatiels, and Senegal parrots who all used more sounds than phrases. Cockatiels are well-known in the pet world to be excellent whistlers, and thus it was satisfying to see that our data support that informal information. We suspect that deviations from the typical patterns may represent acoustic learning preferences, templates, or limitations32.Although individual variation was substantial, we nevertheless saw strong evidence that overall vocal learning abilities differed by species. Pacific parrotlets and sun parakeets showed very limited human mimicry, while grey parrots, Amazona parrots, cockatoos, and macaws were generally very accomplished mimics. The patterns that we documented appeas to reflect natural vocal repertoire variation across species. The documented calls of wild parrots generally range from 5 to 15 calls25,33,34,35,36. Several species, however, present additional complexity: yellow-naped parrots (Amazona auropalliata), palm cockatoos (Probosciger aterrimus), and grey parrots all have natural repertoires of more than 25 discrete elements, with additional elements given in duets13,27,37 Members of these three groups, grey parrots, Amazona parrots and cockatoos also had relatively large repertoires in our study. In several of these species (particularly grey parrots) our measure of mimicked “words” (60) was higher than estimates of natural call “elements” (39) in the literature27. This discrepancy suggests that parrots are capable of learning vocalizations with more than 25 elements and, simultaneously, might reflect a sampling bias wherein survey-takers are more likely to report on individuals with high mimicry ability.Parrot species varied in their tendency to improvise new combinations of elements, although most species did rearrange words to some degree. Research shows that parrot vocalization length and structure carry signal content, so there may be selective pressures favoring this ability24,33. If so, then our data suggest that those pressures are strongest in some cockatoos and weakest in sun parakeets and green-cheeked parakeets. In general, species with larger repertoires also showed more vocal flexibility (Fig. 2, Appendix 6). Additionally, wild birds typically use particular vocalizations in set contexts, so the ability to do so is likely to be adaptive24. Previous studies of captive parrots have demonstrated contextual use of mimicked words, both in tutored lab settings and in home-raised birds28,38. In our sample, contextual use of learned sounds was supported across 89% of individuals and most species. Survey-taker responses on this topic are necessarily subjective, so we emphasize that this rate of contextual use should be interpreted as a general estimate. Nevertheless, the data indicated that parrots frequently associated mimicked human sounds with appropriate human contexts. This finding is particularly revealing because the relevant human contexts are, by their nature, outside the range of typical wild parrot experiences. Contextual vocalization use must, therefore, rely on extremely flexible vocal learning mechanisms.Vocal learning variation by ageOn average, birds aged with high confidence were younger than those aged with low or medium confidence. This pattern might indicate that people tend to overestimate the age of captive birds of uncertain age. This pattern might also reflect the facts that older birds are more likely to be wild-caught and that younger birds are more likely to have good hatch-date documentation. In either case, there are few ramifications of inaccurate age estimates relating to vocal behavior because our data gave no evidence that adult vocal mimicry repertoires varied with age. Our analyses of grey parrots confirmed that repertoires expanded through the juvenile phase, but did not show reliable expansion among adults. Studies of wild birds indicate that parrots can learn vocalizations throughout life; such open-ended learning is limited to a subset of vocal learning species, and can generate different outcomes as animals age15. In some species, animals can add new vocal features over the course of a lifetime, leading to repertoire expansion39,40. In other species, animals may replace parts of their repertoire with newly-learned vocalizations, leading to stable vocal production repertoire sizes across age groups39,41. Our data suggest that parrots fit the second pattern; although they are open-ended vocal learners, their adult repertoires change more by element replacement, than by expansion. This does not necessarily imply that vocalizations are “forgotten” through time, but merely that some sounds are no longer used as conditions change42. Many parrot vocalizations function in social coordination with flock-mates22. The fission–fusion nature of parrot flocks creates changing social conditions for each individual over its lifetime43. A vocal replacement model for repertoire learning would allow individuals to adjust their vocal signatures to match new social situations and stop producing vocalizations that are no longer socially relevant11,44.Vocal learning variation by sexOur analyses of the full data set confirmed the generally held understanding that males and females in most species of parrots have similar vocal learning abilities15. We did, however see sex differences in some species that merit future study. First, we found a substantial overrepresentation of males in our sample. This could be interpreted several ways; (1) there are legitimately more males in the parrot pet trade, (2) pet owners are giving us accurate data but are more likely to give us data on males or (3) some bias exists in which pet owners assume their talking parrots are males, rather than females. Possibilities 1 and 2 seem unlikely because after we eliminated all parrots sexed with low confidence, we were left with a nearly 1:1 ratio of males:females in the subset of parrots that were sexed with high confidence. That trend suggests that the male bias in our data comes (at least in part) from a human tendency to label their pet parrots as male when the sex is not clear. Among songbirds, there is a strong tendency to assume that singing birds are male, and a similar bias may hold true for parrots45. It is unclear whether parrots in this study were mislabeled as male because they vocalize or, more simply, because that is the default human tendency for any animal.Although we conclude that some of the male bias in our data is human error, we also saw patterns that suggest real sex differences in vocal learning some species. For example, Pacific parrotlets are a dimorphic species, and all of our sampled birds were sexed by plumage46. Thus, we expect sexing in this species to be fairly accurate. Our data set included 10 males and no females, a bias unlikely to result purely from sampling error. We saw a similar trend in cockatiels for which there was a large overabundance of males in the data set, even among the 17 birds sexed with high confidence. Humans may be more likely to report on parrots that are good mimics. Therefore, the results likely reflect a real-world tendency for male cockatiels to mimic more human sounds than females. Figure 3 suggests that the same might be true for galahs, sulphur-crested cockatoos, rose-ringed parakeets, Senegal parrots, and budgerigars. Existing research supports the idea that sex differences in vocal behavior are important in several of these species. Among galahs, male and female calls evoke different responses47, and patterns of call adjustment vary by sex among budgerigars20. We also note that several of these species (Pacific parrotlets, rose-ringed parakeets, budgerigars, and cockatiels; Appendix 2b) show sex-based differences in both plumage and vocal learning, raising questions about whether those traits co-evolve.In addition to sex-based differences in the tendency to mimic humans, several well-sampled species showed evidence of sex-based differences in repertoire sizes. Particularly interesting are the blue-and-yellow macaws, in which repertoire size was significantly male-biased. We had more females (15) than males (9) in the data set, but males used on average 3–4 times as many mimicry sounds, phrases and words as females did. Galahs and budgerigars showed a similar male-bias in repertoire sizes, matching the trend of males being overrepresented in our data set for those two species. Prior research on galahs and budgerigars has found that males can be more vocal and more flexible with their vocalizations; perhaps these abilities translate to learning more call types20,47. A similar, but weaker, male mimicry increase occurred in rose-ringed parakeets. In only one species, yellow-headed parrots, did females show a significantly larger mimicry repertoire than males in any category (Appendix 5). Interestingly, the tendency to mimic humans (measured as sampling in the data set) and repertoire sizes did not always show the same patterns. Among sulphur-crested cockatoos, cockatiels, and Senegal parrots, males were more likely to show human mimicry, but their repertoires were not larger than the repertoires of females. This suggests that in some species, females may be less likely to mimic vocalizations, but when they do so they have just as large a vocabulary as males.The reported sex differences in parrot vocal mimicry repertoires are intriguing, but also are tentative conclusions. In many species, including our best sampled species, grey parrots, we saw no evidence of sex-differences in repertoire size. The sex-biases that we did document lose statistical significance after controlling for the many comparisons that we conducted. Nevertheless, we expect that some of our data represent true biological differences, especially because studies of wild birds have shown similar trends47,48. Thus, we offer our data as a starting point for additional research. Taken together, the analyses by sex provide interesting points of comparison to other vocal learning animals. Our combined analyses suggest that sex differences in vocal learning are vastly smaller and less common among parrots than they are among oscine passerines and hummingbirds45,49,50. Sex-based patterns of vocal learning in parrots appear more similar to those of vocal learning mammals than to those of other vocal learning birds51. Overall, parrots and songbirds present excellent comparative study systems for all aspects of sex differences in song learning, from the mechanistic to the functional17,51.Vocal learning variation by social contextMany parrot vocalizations function in social organization for individuals within flocks, and the ability to learn from conspecifics is essential to parrot familial and social integration12,15,52. Although our study specifically examined vocal learning of human sounds, we thought it possible that the presence of other parrots would increase mimicry rates if parrots learned human vocalizations from their parrot companions. Anecdotal stories of parrots teaching words to other parrots abound53, and studies of grey parrot cognition show that vocal modeling by multiple tutors can lead to better learning of human words54. Most existing results, however, are based on human tutoring, with controlled studies of parrot-parrot word transmission lacking. Here we tested whether social interactions with other parrots correlated with more vocal learning of human sounds. Our data gave no evidence that parrot-parrot social interactions drive human vocal mimicry. This was true across the full sample (controlling for species identity), and for our best sampled species, grey parrots. Although companion parrots are known to learn from conspecifics, that learning does not appear to shape repertoire sizes53. Open questions remain about whether signal complexity, repertoire size, or aspects of vocal learning covary with social complexity at a larger scale among parrots55. Follow up studies should address these questions using phylogenetically-controlled methods56. More

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    Algal sensitivity to nickel toxicity in response to phosphorus starvation

    Effect of phosphorus starved cultures of Dunaliella tertiolecta on growth represented as optical density under stress of nickel ionsIn the case of normal culture, phosphorus starved control culture (without nickel stress), and phosphorus-starved treated cultures, data presented in Table 1 and graphed in figure (S1, Supplementary Data) clearly showed a progressive increase in optical density with increasing culturing period in case of normal culture, phosphorus-starved control culture, and phosphorus-starved treated cultures. Our findings are consistent with those of18 who found that in phosphorus starved cultures of three algae species, Microcystic aeruginosa, Chlorella pyrenoidesa, and Cyclotella sp., the biomass, specific growth rate, and Chl-a all declined significantly.The optical density achieved during the four periods of culturing was lower in phosphorus-depleted control cultures than in normal cultures (i.e., cultures contained phosphorus). When compared to a normal control (without nickel addition), the optical density was reduced by 9.1% after 4 days of culturing under phosphorus deprivation and by 10.0 percent after 8 days of culturing. In the case of 5 mg/L dissolved nickel, however, the obtained optical density values in phosphorus starved treatment cultures rose with the increase in culturing period during all culturing periods as compared to phosphorus-starved control (without nickel addition) cultures.At 10 mg/L dissolved nickel and after 4 days of culturing, the optical density although less than those in case of concentration 5 mg/L, yet it was higher than control (− P) but by increasing the culturing period more than 4 days, the optical density was less than control (− P). Our results are similar to those of19 who observed that the decrease in cell division rate signaled the onset of P-deficiency. The cultures that showed no significant increase in cell number for at least three consecutive days under the experimental conditions were considered P-depleted. In addition20, observed that the growth rate of Dunaliella prava was found to be dramatically lowered when phosphorus was limited. The content of chlorophyll fractions, total soluble carbohydrates, and proteins all fell considerably as a result of phosphorus restriction.The results concerning the effect of dissolved nickel on the growth of Dunaliella tertiolecta under conditions of phosphorus limitation show that phosphorus starved Dunaliella had lower growth as compared to the control (phosphorus-containing culture medium). These results are in agreement with those obtained by7 who reported that the optical density of Chlorella kessleri cell suspension decreased with phosphorus deficiency compared to control. Also21, found that Chlorella vulgaris cells grew 30–40% slower in phosphorus-starved cultures than in control cultures. Furthermore22, showed that diatoms were unable to thrive when phosphorus levels were insufficient. Diatom dominances were reduced to 45 and 55% in enclosures where phosphate was not provided23 observed that, under salt stress, Chlorella’s metabolic rate was substantially lower than Dunaliella’s.It can be concluded that when microorganisms are deprived of phosphorus, dissolved nickel uptake decreases, resulting in an increase in algal metabolism24. Also25, examined the effects of phosphorus and nitrogen starvation on the life cycle of Emiliania huxleyi (Haptophyta) and proved that various biochemical pathways’ metabolic load increased under P-starvation while it decreased under N-starvation.Effect of phosphorus starved cultures of Dunaliella tertiolecta on chlorophylls content under stress of nickel ionsTable 2 and figure (S2, Supplementary Data) show the sequences of change in the amount of chlorophylls a and b in phosphorus-depleted cultures of Dunaliella tertiolecta in response to various dissolved nickel concentrations. The results show that total chlorophyll content rose steadily until the end of the experiment under normal conditions (a control containing phosphorus). These results are in harmony with those obtained by24. The ratio between chlorophylls “a” and “b” remained nearly constant till the end of the 12th day. At the 16th day of culturing, the ratio decreased from 2.9:1 to 2.4:1. On the contrary, the total chlorophylls under control (in the absence of nickel element) in case of phosphorus-starved cultures showed a progressive increase up to the 12th day. At the 12th day the total chlorophylls in case of phosphorus-starved cultures decreased by 10.7% compared to the normal control. At the 16th day, the total chlorophylls in case of untreated phosphorus starved culture decreased by 20.8% compared to those obtained at normal control26. Reported that the chlorophyll content of Chlorella sorokiniana was significantly reduced due to a lack of nitrogen and phosphorus in the medium.Table 2 Effect of different concentrations of dissolved nickel (mg/L) on chlorophylls content (µg/ml) of Dunaliella tertiolecta under the stress of phosphorus starvation.Full size tableThe total chlorophyll content of Dunaliella tertiolecta in the phosphorus-starved cultures treated with 5 mg/L of dissolved nickel increased gradually until the 12th day, when the content of the total chlorophylls reached 2.11 µg/ml, i.e., higher than the phosphorus-starved control (− P) by 15.3%. At the 16th day, the total chlorophylls, although lower than those obtained at the 12th day, were still higher than the control (− P). At a concentration of 10 mg/L of dissolved nickel, slight increase in the content of total chlorophylls was recorded from the beginning to the end of the culturing period, i.e., from the 4th to the 16th day. At the other concentrations of dissolved nickel (15, 20, and 25 mg/L), a pronounced decrease in the total chlorophylls could be observed from the 4th to the 16th day of culturing compared to control (− P). Our results are going with an agreement with those obtained by27 who found that chlorophylls were inhibited maximum at higher dissolved nickel concentrations but activated at lower values. The normal ratio between chlorophylls “a” and “b” (3:1) was upset after the 8th day of culturing under concentrations 5, 10, and 15 mg/L of dissolved nickel. At 20 and 25 mg/L of dissolved nickel, this ratio was unstable from the beginning to the end of the experiment. The fact that dissolved nickel is extremely mobile and hence only absorbed to a minimal level may explain the sensitivity of the tested alga to nickel in response to phosphorus deficiency, and an increase in phosphorus concentration favors its absorption by microorganisms28. It can be concluded that when microorganisms are deprived of phosphorus, dissolved nickel uptake decreases, resulting in an increase in algal metabolism.Effect of different concentrations of dissolved nickel on photosynthesis (O2-evolution) of phosphorus starved cells of Dunaliella tertiolecta
    Data represented in Table 3 and graphed in figure (S3, Supplementary Data S3) showed that the effect of phosphorus limitation on the photosynthetic activity of Dunaliella tertiolecta in response to five different concentrations of dissolved nickel revealed that, under phosphorus limiting conditions, the amount of O2-evolution was lower than in untreated cultures (the control). The evolution of O2 after 4 days of culturing in case of phosphorus starved control decreased by 8.7% compared to normal control, while after 12 days it decreased by 30.4%. The rate of O2-evolution at different concentrations of dissolved nickel over 5 mg/L caused successive reductions in the O2-evolution of phosphorus starved cells. Application of 5 mg/L of dissolved nickel, the results cleared that the rate of O2-evolution increased under the effect of all tested concentrations till the end of the experiment. It is clear from our data that the rate of O2-evolution depended mainly on the concentration of the nickel element and the length of culturing period. The lower the rate of O2-evolution, the higher the element’s concentration, and the longer the culturing period. This coincided with the findings of7 who found that low phosphorus treatment causes Chlorella kessleri to lose its photosynthetic activity. In this regard, it was discovered that phosphorus deficiency resulted in a decrease in photosynthetic electron transport activity29 found that the O2-evolution of Chlamydomon reinhardtii declined by 75%. This decrease reflects damage of PSII and the generation of PSII QB-non reducing centers.Table 3 Effect of different concentrations of dissolved nickel (mg/L) on photosynthetic activity (O2-evolution calculated as µ mol O2 mg chl-1 h-1) on phosphorus supplemented and starved cells of Dunaliella tertiolecta.Full size tableAlso30 found that P- deficiency has been correlated with lower photosynthetic rates. In the case of the treated phosphorus-starved cultures with lower concentrations (5 mg/L) of dissolved nickel, the rate of photosynthesis increased when compared to the phosphorus-starved control, but was less than that of the normal control (without nickel treatment). On the contrary, it was found that, in the treated phosphorus-starved cultures at concentrations of 10, 15, 20 and 25 mg/L of the tested element, the rate of photosynthesis decreased from the beginning to the end of the experiment. With increasing concentration, duration of the culturing period, and kind of element, the condition of decrease in O2-evolution became more pronounced; the same results were also recorded by24. The stimulation of growth and photosynthesis in the presence of some concentrations of dissolved nickel under phosphorus-limiting conditions is observed by31 they report that in Cu2+ sensitive Scenedesmus acutus, intracellular polyphosphate plays a key role in shielding photosynthesis from Cu2+ toxicity but not in copper resistant species.Effect of different concentrations of dissolved nickel on respiration (O2-uptake) of phosphorus starved cells of Dunaliella tertiolectaData obtained in Table 4 and graphed in figure (S4, Supplementary Data S4) concerning the rate of respiration of Dunaliella tertiolecta under phosphorus-limiting conditions was higher than that of untreated phosphorus-starved (control) for a short period of time only, i.e., after 4 days, at concentrations 5, 10 and 15 mg/L of dissolved nickel, After 8 days of culturing, the rate of O2- uptake increased only at 5 mg/L of dissolved nickel, while at the other concentrations it decreased gradually with increasing the concentration of the element. This finding is consistent with the findings of23, who discovered that Dunaliella cells increased their O2 absorption and evolution rates in the presence of 2 M salt NaCl in the media. In terms of oxygen uptake rate, Dunaliella cells demonstrated an increase in salt concentrations. In 1.5 M NaCl, it increased significantly by 60–80%.Table 4 Effect of different concentrations of dissolved nickel (mg/L) on respiration activity (O2-uptake calculated as µ mol O2 h-1) on phosphorus supplemented and starved cells of Dunaliella tertiolecta.Full size tableConcerning the increase in respiration in P-depleted green alga species cultures5 suggested that Scenedesmus, for example, can utilize the energy stored in starch and lipids for active phosphorus uptake from lake sediments. This process is aided by an increase in phosphatase production32 and these cells’ ability to operate anaerobically33. When unicellular green algae or higher plants are exposed to P deficiency, the majority of newly fixed carbon appears to be allocated to the synthesis of non-phosphorylated storage polyglucans (i.e., starch) or sucrose, with less photosynthetic activity directed to respiratory metabolism and other biosynthesis pathways34. It can be concluded from the obtained results that, when the alga was cultivated under phosphorus deficiency and treated with varied amounts of dissolved nickel, the growth was the most sensitive characteristic, followed by photosynthesis, and then dark respiration. In the few comparative studies with several species of green algae, growth was more sensitive than the other physiological processes examined. Out of them35, reported that growth was more susceptible to phosphorus deficiency in Chlorella pyrenoidosa and Asterionella gracilis than photosynthesis and respiration (the least sensitive processes). Growth was also more sensitive than photosynthesis in Nitzschia closterium 36 . Another important fact reported by37 is that under low phosphorus conditions, Dunaliella parva accumulates lipids rather than carbohydrates. These findings imply that phosphorus stress may prevent starch and/or protein production, leading to an increase in carbon flux to lipids. More

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    Carbon turnover gets wet

    Whether land acts as a carbon sink or source depends largely on two opposite fluxes: carbon uptake through photosynthesis and carbon release through turnover. Turnover occurs through multiple processes, including but not limited to, leaf senescence, tree mortality, and respiration by plants, microbes, and animals. Each of these processes is sensitive to climate, and ecologists and climatologists have been working to figure out how temperature regulates biological activities and to what extent the carbon cycle responds to global warming. Previous theoretical and experimental studies have yielded conflicting relationships between temperature and carbon turnover, with large variations across ecosystems, climate and time-scale1,2,3,4. Writing in Nature Geoscience, Fan et al.5 find that hydrometeorological factors have an important influence on how the turnover time of land carbon responds to changes in temperature. More

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    Phytoplankton in the middle

    Marine phytoplankton both follow and actively influence the environment they inhabit. Unpacking the complex ecological and biogeochemical roles of these tiny organisms can help reveal the workings of the Earth system.
    Phytoplankton are the workers of an ocean-spanning factory converting sunlight and raw nutrients into organic matter. These little organisms — the foundation of the marine ecosystem — feed into a myriad of biogeochemical cycles, the balance of which help control the distribution of carbon on the Earth surface and ultimately the overall climate state. As papers in this issue of Nature Geoscience show, phytoplankton are far from passive actors in the global web of biogeochemical cycles. The functioning of phytoplankton is not just a matter for biologists, but is also important for geoscientists seeking to understand the Earth system more broadly.Phytoplankton are concentrated where local nutrient and sea surface temperatures are optimal, factors which aren’t always static in time. Prominent temperature fluctuations, from seasonal to daily cycles, are reflected in phytoplankton biomass, with cascading effects on other parts of marine ecosystems, such as economically-important fisheries. In an Article in this issue, Keerthi et al., show that phytoplankton biomass, tracked by satellite measurements of chlorophyll for relatively small ( More