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Drag coefficients of plesiosaurs, ichthyosaurs and modern cetaceansAt equal Reynolds numbers (same body length and same flow velocity), the total drag coefficients of plesiosaurs (Cd) are higher than the estimated values for ichthyosaurs and modern cetaceans (Fig. 1a). The limbless bodies, however, display similar Cd in all three groups and are even lower-than-average in the long-necked plesiosaurs, indicating that the limbs are responsible for the observed high Cd. The limbs of plesiosaurs contribute to more than 20% of their total drag coefficient: up to 32.2% in the basal Meyerasaurus and averaging 25% in derived plesiosaurs, with no major differences between plesiosaur morphotypes. In parvipelvian ichthyosaurs the contribution of the limbs to Cd is 11.2–15.6%, compared to 8.7–14.3% in modern cetaceans. Some of the living taxa we include provide a functional reference for this analysis. Our computed drag coefficient for the bottlenose dolphin model (Cd = 0.00413 at Re = 107) for example, is consistent with the estimates from a gliding living dolphin33 (Cd = 0.0034 at Re = 9.1 × 106) and other static CFD simulations34 (Cd = 0.00413 at Re = 107). It is worth noting that these values are, as expected, lower than estimates obtained from kinematic models, as motion is not accounted for35. In a former study, drag coefficients for a plesiosaur (Cryptoclidus), two ichthyosaurs and various cetaceans were obtained from rigid models in water tanks36. However, the pressure drag component (Cp) was likely overestimated due to the proximity of the models to the air–water interface, and thus are not directly comparable to ours.Fig. 1: Comparison of the drag coefficient of derived plesiosaurs, ichthyosaurs and cetaceans.a Total drag coefficient computed for the full models including the limbs (‘body + limbs’, circles) and the limbless models (‘body’, squares). Average (point) and range (bar) shown for calculations at Re = 5 × 106–107. The derived short-necked plesiosaurs are highlighted in orange; the parvipelvian ichthyosaurs in blue and the extant cetaceans in red. A basal plesiosaur included as a reference is highlighted in purple. b Representative two-dimensional plots of the flow velocity magnitude at Re = 5 × 106 (inlet velocity of 5 ms−1) in lateral view. For dorsal view see Supplementary Fig. 1. Images of Tursiops and the three ichthyosaurs modified from Gutarra et al.29.Full size imageIn all models across the various clades, velocity plots display a stagnation point at the anterior tip of the model, a thin velocity gradient along the body corresponding to the boundary layer, an area of higher velocity around the greatest diameter and a low velocity wake behind the body, characteristic features of a fully developed external flow (Fig. 1b, Supplementary Fig. 1). The acceleration of flow results in areas of low pressure (Supplementary Fig. 2), while high pressure areas are observed where stagnation occurs. Our CFD methodology has been previously validated against experimental data from slender torpedo-like shapes26 and has been shown to provide a reliable distribution of internal drag components29 essential when dealing with streamlined bodies35. In all our simulations, the proportion of frictional and pressure drag was consistent with the expected values for slender geometries31: most of the drag originated from skin friction with a minor pressure drag component (Supplementary Fig. 2). The relatively larger limbs of plesiosaurs (Supplementary Table 1) produce a small increase in skin friction (Supplementary Fig. 2a), but a large increase in the pressure drag coefficient (Supplementary Fig. 2b), indicating that the latter largely explains differences in total drag coefficient between the groups. These effects might be explained by the low local Reynolds number of the flippers (resulting from a small chord length) producing high local Cd relative to the rest of the body31, alongside interference drag (i.e. drag caused by the interaction of flow fields where limbs and body meet), which might be higher for larger flippers.Effect of body shape and body size on drag-related costs of steady swimmingWhen comparing morphologies at the same volume (proxy for body mass) and the same velocity, to focus on the effect of shape alone, derived plesiosaurs produce on average 30% more drag than parvipelvian ichthyosaurs and modern cetaceans (Fig. 2a, Supplementary Table 3; two-sample t-tests p  0.05). In these conditions, the drag-related costs of steady swimming of plesiosaurs fall within the range observed in both modern cetaceans and ichthyosaurs. Normalised against a 2.85 m-long Tursiops, the COTdrag for derived plesiosaurs ranges from 0.42, estimated for the large elasmosaur Thalassomedon, to 1.41 in the medium-sized Dolichorhynchops. In the parvipelvians, COTdrag spans from 0.33 estimated for the large Temnodontosaurus, to 1.76 in a 2.5 m-long Stenopterygius. Cetaceans show a smaller lower limit, because they include the largest animal in our sample, a 16 m-long humpback whale, with a COTdrag of 0.13 compared to Tursiops. The estimated cetacean upper COTdrag limit is 1.54 for a 1.9 m Tursiops. On the other hand, comparisons of the total drag power (Pdrag, i.e., the non-mass normalised version of COTdrag) for the same speed of 1 ms−1 (Fig. 3), show a different trend. Pdrag is highest for Megaptera, higher than in any fossil taxa included in this study, and is lowest in Tursiops. Thalassomedon is comparable both in total drag power and COTdrag to the killer whale. Similarly, the thalassophonean pliosaurid Liopleurodon matches the elasmosaurian Hydrotherosaurus in having a similarly low mass-normalised COTdrag but requiring about 4× more total drag power than Tursiops. Smaller forms like the polycotylid Dolichorhynchops and the thunnosaurian Ophthalmosaurus resemble the extant bottlenose dolphin in having a relatively high COTdrag and low total power.Fig. 3: Comparative plot of mass-normalised drag power and total drag power.Values of mass-normalised drag power (i.e., drag per unit of volume or COTdrag calculated as in Fig. 2b) in grey, and non-mass-normalised total drag power, in black, for an array of derived plesiosaurs, parvipelvian ichthyosaurs and modern cetaceans compared at the same inlet velocity of 1 ms−1. Error bars represent minimum and maximum values accounting for taxon body size variation (see Supplementary Data). Values are normalised to the results for Tursiops.Full size imageThus, in contrast to the volume-normalised simulations, differences between animals at their life-size scale are mainly influenced by size. For example, medium-sized plesiosaurs and ichthyosaurs, such as Dolichorhynchops and Ophthalmosaurus, have values of COTdrag close to that of a dolphin, while large plesiosaurs like Thalassomedon are more like the parvipelvian ichthyosaur Temnodontosaurus and a modern Orcinus. It is worth noting that the inflow velocity of 1 ms−1, is a reference velocity used for comparative purposes, and is not equivalent to the optimal cruising speed (i.e. speed at which COT is minimum16). This parameter is known to vary little in nature, with most vertebrates displaying values of preferred speed between 1–2 ms−1 regardless of body size40,41,42, which means it is reasonable to assume all tested taxa, regardless of their size, were able to swim at this velocity. Using a different reference velocity (2 ms−1) has no effect on the relative values of drag per unit of volume and the mass-normalised drag power (Supplementary Fig. 3; Supplementary Data). A reduction of mass-normalised drag-related costs of cruising as body size increases is selectively advantageous, as energy savings can be used to extend foraging and mating range, increase swimming speed and fuel other activities42,43.Our analysis shows that for highly aquatic tetrapods, size dominates over shape in affecting the drag-related costs of steady locomotion. This is because COTdrag (i.e., the balance of drag to volume) is highly sensitive to surface/volume proportion (Fig. 2f), and so is much influenced by isometry in streamlined animals.Interplay between neck anatomy and body size in plesiosaur dragSimulations at constant Reynolds number (i.e., comparing models at same total length and same flow velocity), show that necks up to 5× the length of the trunk do not increase substantially the total drag coefficient. Longer neck ratios up to 7× were found to impact the drag coefficient by as little as 3% (Fig. 4a). We estimated a 4–10% increase in skin friction drag coefficient for neck ratios of 3–7×, but also a comparable reduction in pressure drag resulting in almost no change in the total drag coefficient. A previous CFD-based study also found no differences in drag coefficient between plesiosaur models with variable neck proportions20, but further comparison is not possible because of great differences in the order of magnitude of Cd, the use of a different scaling reference area and the lack of information on skin and pressure drag20. Here, we have shown that long necks produce only a small increase in skin friction, although not as great as previously speculated25,30, and this is nullified by reduced pressure drag.Fig. 4: Influence of neck length and its interaction with body size on the drag-related costs of swimming in plesiosaurs.a Total drag coefficient and skin friction drag coefficient for an array of hypothetical plesiosaurs with varying neck ratios computed at Re = 5 × 106 (same total length and inflow velocity). b Drag per unit of trunk volume computed for the same array of models scaled at the same trunk length and tested at the same speed of 1 ms−1. The hypothetical models were created by modifying the length in the model of the basal plesiosaur Meyerasaurus victor which has a neck ratio of 0.87×. The limits of the trunk (which extends along the torso and includes the edges of the pectoral and pelvic girdles) are shown in red in the rendered models. c Three-dimensional models of a wide array of plesiosaurs, in dorsal view, at their life-size dimensions, showing the differences in body proportions and sizes. The limits of the trunk in the models (defined as in b) are coloured by group. Basal plesiosaurs are highlighted in purple. Among the derived groups, thalassophonean plesiosaurs (derived pliosaurid plesiosaurs) are highlighted in light orange, polycotylid plesiosaurs in dark orange and elasmosaurid plesiosaurs in green. d Scatterplot of trunk length (cm) and neck ratio showing the relative drag per unit of trunk volume as a gradient of colour for each taxon analysed and for the plot area in between (contour lines represent the interpolated values of drag per unit of volume). e Plot of the relative drag per unit of trunk volume versus the trunk length showing results highlighted by group. Line plots at the right-hand side show the range for each group. The D/Vtr and the trunk length show a significant negative correlation (Pearson’s correlation coefficient calculated with log-transformed variables, p = 2.28 × 10−7, R2 = −0.92). A small version of the fitted power curve (regression equation (y=69.76{x}^{-0.94})) is shown on the right upper corner. The grey area around the curve represents a confidence interval of 95%. All values in b, d and e are normalized to the results for the Meyerasaurus model.Full size imageNext, we explored the impact of neck proportions on drag-related costs of swimming in simulations where the size factor is removed. We found that if trunk dimensions are kept constant while the neck is enlarged, the drag per unit of trunk volume does not change appreciably for neck ratios up to 2×. However, longer neck proportions did impact resistive forces. This was moderate for a 3× ratio, with 12% more drag per unit of trunk volume, but became more substantial for longer necks, with 22%, 35% and 59% excess drag for necks of 4×, 5× and 7× respectively (Fig. 4b). This means that elasmosaurine elasmosaurs, with necks commonly 3–4× the length of the trunk23 might have experienced higher drag than other plesiosaurs of similar trunk dimensions.To test if the ‘long neck effect’ remains when body size is accounted for, we compared the relative amount of drag-per-unit-trunk-volume (D/Vtr) in a wide sample of plesiosaurs (Fig. 4c) at life-size scale for a constant velocity of 1 ms−1, including three species with neck ratios above 2×: Styxosaurus (2.76×), Hydrotherosaurus (3.18×) and Albertonectes (3.72×), the last being the elasmosaur with the longest reported neck44. Our results show great variability in D/Vtr. Small-bodied plesiosaurs such as Plesiosaurus, Meyerasaurus and Dolichorhynchops generated up to six times more D/Vtr than the largest plesiosaurs, Kronosaurus and Aristonectes (Fig. 4d, e). Comparisons per group show that both basal plesiosaurs and derived polycotylids, the groups with the smallest specimens, produced generally higher D/Vtr. Moreover, we did not find substantial differences between elasmosaurs and thalassophonean pliosauroids (Fig. 4e, Supplementary Table 4; all two-sample t-tests p  > 0.05). Both groups had similarly low ranges of D/Vtr regardless of neck length, lower on average than in polycotylids. These results stand even if we exclude Aristonectes, which belongs to the aristonectines, an elasmosaur subfamily with reduced neck length23,45. Further comparisons by morphotype show no significant differences between short-necked pliosauromorphs (here arbitrarily including plesiosaurs with neck ratios below 2×) and long-necked plesiosauromorphs (Supplementary Table 4, all two-sample t-tests p  > 0.05). The highest values of D/Vtr occur in animals with trunk lengths of 100 cm or less, followed by a steep decrease between 100–150 cm and a steadier decrease in longer trunks. This indicates a strong negative correlation between trunk dimensions and D/Vtr (Pearson’s product-moment correlation between the log-transformed variables, adjusted r2 = −0.92, p = 2.28 × 10−7). The curve that best describes this relationship is the power equation, D/Vtr = 69.76 × Trunk length−0.944 (Fig. 4e), an almost inversely proportional relationship, consistent with the streamlined nature of these animals for which skin friction drag is dominant.Polycotylids and thalassophonean pliosaurs, both derived pliosauromorph plesiosaurs9,21, share the same general body proportions9,21,46, but the latter had larger bodies and therefore needed less power in relation to their muscles to move at the same speed. Elasmosaurs on the other hand, despite their disparate morphologies, were no different from thalassophonean pliosaurs in their drag-related costs of forward swimming (Fig. 4c–e) and therefore they were likely to have been equally efficient cruisers.Earlier research suggested that, even if long necks did not add extra drag during forward swimming, speed in elasmosaurs would have been limited to avoid added drag when their necks bent20. However, when the neck is bent in living forms, the course of swimming changes, as does the flow direction, but the body remains streamlined in the direction of incoming flow. For example, sea lions perform non-powered turns initiated by the head in which the body glides smoothly in a curved position, limiting deceleration47. Further biomechanical research is needed to understand the role of plesiosaur necks in manoeuvrability and other aspects of swimming performance, as well as how these were influenced by shape and flexibility. The well-established idea that long-necked plesiosaurs were sluggish, slow swimmers7,30 is thus not supported here, not because long necks did not increase drag20, but because body size overrode this drag excess.Long necks evolved in large-bodied plesiosaurs: implications for dragWe analysed trends of body size and neck proportion in a wider sample of sauropterygians, including plesiosaurian and non-plesiosaurian Triassic sauropterygians. Long necks (neck ratio > 3×) occur in taxa with trunk lengths > 150 cm, whereas most sauropterygians had neck ratios of ≤ 2× (Fig. 5a). The great plasticity of body proportions of sauropterygians before and after their transition to a pelagic lifestyle after the Triassic has been well documented21,23,46, but this is the first time that neck and body size have been explored in the context of swimming performance for such a wide sample. We show that overall, sauropterygians and particularly plesiosaurs, mainly explored neck morphologies with little or no effect on drag costs and did not enter morphospaces that were suboptimal for aquatic locomotion (i.e., corresponding to small trunks with long necks; Fig. 5a). In fact, ancestral state reconstruction for trunk length shows that the ancestor of elasmosaurs was likely around 180 cm long and had a relatively short neck with a ratio smaller than 2× (Fig. 5b, c). This indicates that large trunks preceded neck elongation in elasmosaurs and suggests that extreme proportions might have been favoured by a release of hydrodynamic constraints.Fig. 5: Evolutionary trends of neck proportions and body size in Sauropterygia and their implications for the drag-related costs of swimming.a Bivariate plot of the length of trunk and the neck ratio of 79 sauropterygian taxa. Polygons in different colours show area occupied by the main sauropterygian groups. The functional trends describing the effect of each axis are based on results from flow simulations. On the top of this graph, a univariate plot shows the distribution and mean values of trunk length for each group. b, c Phenograms showing the disparity of trunk length (b) and neck ratio (c) in sauropterygians through time. The branches corresponding to basal Plesiosauria (including Rhomaleosauridae and Plesiosauridae), thalassophonean pliosaurs, polycotylids and elasmosaurs are highlighted (colour coding as in a). d, e Sauropterygian trees showing the evolutionary rates for trunk length (d) and neck ratio (e) represented by colour gradient (see Supplementary Fig. 5 for an alternative analysis to 5d using the log10-transformed trunk length). Consensus trees show average results from analyses of 20 cal3-dated trees (see Supplementary Figs. 4 and 6 for analysis on Hedman-dated trees). Rates are based on the mean scalar evolutionary rate parameter.Full size imageWe next explored evolutionary rates of relative neck length and trunk length in sauropterygians. The pattern of trunk length evolution is consistent with a heterogeneous rates model, not a homogeneous Brownian motion model (log Bayes Factor48 (BF)  > 5 in 100% of the sampled trees and > 10 in 92.5%, Supplementary Table 5). Analysis of non-transformed trunk data shows that through the evolution of Sauropterygia, there was a general increase in trunk length with some higher rates, in Triassic nothosauroids, Jurassic rhomaleosaurids and Cretaceous aristonectine elasmosaurs (Fig. 5d; Supplementary Fig. 4a). Additionally, analysis of the log10-transformed trunk data highlights variation in the small-to-medium size ranges and reveals high rates in Triassic eosauropterygians (Supplementary Figs. 5 and 6). The largest trunks evolved independently in two groups, thalassophonean pliosaurids and elasmosaurid plesiosauroids, with no evidence of high rates in the former. In the plesiosauroids, rates are not particularly high in the basal branches, but they are very high in derived aristonectines, and rates for the whole clade were significantly higher than the background rate in 40% of randomisation tests (Supplementary Fig. 7 and Table 6). A progressive increase in body mass over evolutionary time has been described for various clades of aquatic mammals49 and seems to be a common hallmark of the aquatic adaptation to marine pelagic lifestyles in secondarily aquatic tetrapods44. Whether body size reaches a plateau as is the case in cetaceans49 and what constraints influence the evolutionary patterns of size in plesiosaurs remains unexplored. Against this general trend, some derived plesiosaurs, such as polycotylids, saw a reduction in body size, which might have been related to pressures on niche selection, such as adaptation to specific prey, the need for higher manoeuvrability or other ecological factors. As shown earlier, small sizes require lower amounts of total power for a given speed, and therefore would be favoured if for example food resources were limited. This suggests that, in spite of the energy advantages of large size in terms of reduced mass-specific drag29 and metabolic rates49,50, which make it a common adaptation to the pelagic mode of life, other constraints limiting very large sizes were also at work50,51.A heterogeneous evolutionary rates model for neck proportion is also strongly supported (log BF  > 5 in 100% of the sampled trees and > 10 in 45%, Supplementary Table 5). Fast rates are consistently seen at the base of Pistosauroidea (including some Triassic forms and plesiosaurs) and, interestingly, also within elasmosaurs (Fig. 5e; Supplementary Fig. 4b). The neck proportions of elasmosaurs were found to evolve at a faster pace than the background rate in 90% of analyses (randomisation test p-value < 0.001 in 80% and < 0.01 in 10% of the sampled trees; Supplementary Fig. 7 and Table 6). Very fast rates in elasmosaurs are concentrated in the most derived branches (i.e., Euelasmosauridia from the late Upper Cretaceous52) and represent both rapid neck elongation in elasmosaurines and rapid neck shortening in weddellonectians (i.e., aristonectines and closely related taxa52). Additionally, various other independent instances of relative shortening of the neck occurred during the evolution of Sauropterygia, most notably in placodonts, pliosaurs and polycotylids, but these are not associated with high rates.Our findings contrast with a previous study23 which did not identify any significant evolutionary rate shifts in the neck ratio across Sauropterygia. Here we use a larger number of taxa and a different model fitting approach, which might account for these discrepancies. The association between very long necks and large trunks, along with our flow simulations results and the evidence of high rates in the elongation of necks in elasmosaurines (Fig. 5e), suggests that neck elongation was facilitated by large body sizes. The question remains why neck ratios did not evolve longer than 4×. According to our data, hydrodynamic constraints might have operated against the selection of such long necks. However, it is possible that the primary function for which they were selected, which is still debated30,53, did not require necks with those characteristics. Neck anatomy is likely to be the result of a compromise between different functions/constraints, one of them being hydrodynamic, as shown by the results presented herein. More

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Relevance for design of dFAD recovery programmesOur results provide guidance for implementing effective dFAD recovery programmes. More than 40% of dFAD trajectories in the Indian and Atlantic oceans drifted away from fishing grounds never to return, potentially later stranding in coastal areas (Imzilen et al.5 estimated that 10–20% of all French dFADs eventually strand, whereas 16.0% of our trajectories that definitively leave fishing zones strand). This loss represents at least 529 tonnes yr−1 of marine litter for the French fleet5,14 and probably 2–3 times that weight including all purse seiners in the two oceans28. More than 20% of dFAD trajectories that drifted away from fishing grounds passed within 50 km of a port (ranging from 3.3% to 31.6% for cut-off distances from 10 to 100 km; potentially underestimated due to remote deactivation of GPS buoys by purse seiners). This result suggests that coastal dFAD recovery programmes could be complementary to other mitigation measures, such as dFAD buoy limits already implemented by tRFMOs and spatio-temporal dFAD deployment closures proposed by Imzilen et al.5. Indeed, Imzilen et al.5 showed that prohibiting dFAD deployments in areas that would probably lead to strandings would principally protect coastal areas of the southwestern Indian Ocean and the eastern Gulf of Guinea, whereas we found that dFADs exiting fishing grounds from other areas, such as the northwestern Indian Ocean and the northern Gulf of Guinea, passed close to regional ports and could potentially be recovered at sea. Although our results are specific to the French and associated purse-seine fleet (representing ~1/3–1/2 of catch and dFAD deployments of all fleets28), available data indicate that other purse-seine fleets have similar spatio-temporal patterns of deployments28, suggesting that our results are applicable to the entire tropical tuna purse-seine fishery in the Indian and Atlantic oceans.These results contrast somewhat with existing analyses from the western and central Pacific Ocean, where it was estimated that 36% of dFADs ended up outside fishing grounds, but that the final recorded position of these abandoned dFADs were typically far from ports (502–952 km)29. Although these differences may be related to the larger spatial scales of the Pacific Ocean, additional analyses based on examinations of entire trajectories are needed to assess viability of recovery programmes based on ports.Consequences of spatial and temporal variation of dFAD lossHigh seas recovery could also be structured around our results on where important percentages of buoys exit fishing grounds towards the high seas. In the Indian Ocean, dFADs definitively leaving from the eastern border (70° E) end up stranded in or transiting through the Maldives and the eastern Indian Ocean. This happens relatively less frequently in the period from June to August and becomes much more frequent from October to December. Low loss rates during June to August are consistent with known seasonal patterns in dFAD deployment and fishing during this period4,25. At that time of the year, dFADs are deployed by fishers with the intent that they drift along the eastern African coast until they reach the main dFAD fishing grounds off Somalia, avoiding strong monsoon-driven currents favourable to eastward export of dFADs from July to December27. This is followed by a more intense dFAD fishing season during August–October. Finally, starting in October/November, a period of transition towards fishing further south in the Indian Ocean occurs, with relatively more focus on free-swimming school sets25,30, probably contributing to abandonment of dFADs in the northern Indian Ocean in the last quarter of the year.In the Atlantic Ocean, dFADs lost to the high seas exit fishing grounds mostly from the northwestern border (between 10° and 20° N) and southwestern border (2°–5° S), which is consistent with transport by the North Equatorial and South Equatorial Currents26. Although the seasonality of loss is less marked in the Atlantic Ocean than in the Indian Ocean, the peak months of July and December are associated with transitions in the spatio-temporal distribution of deployments from principally deploying just north of the equator off of West Africa to focusing on the Gulf of Guinea further east30. These transitions could lead to increased dFAD abandonment in areas highly susceptible to export of dFADs, although seasonality in currents may also play a role.Challenges facing recovery programmesWhile the information provided in this paper on spatio-temporal patterns of dFAD loss provides an essential foundation for implementing dFAD recovery strategies, there are several important practical challenges to the success of such efforts. Most efforts towards reducing or removing marine debris after it has been created have so far focused on beach clean-ups31,32. Such operations are costly, time-consuming and only capture a fraction of the overall debris18,33. Recovery at sea is a promising alternative solution34, but this requires consolidating systems to observe these debris35 and understanding their drift36, as well as putting in place appropriate incentives and socio-economic and political frameworks37. Broadly, data availability (for example, access to near-real-time location data from all fleets), equipment availability (for example, appropriately sized and equipped vessels for collecting large debris such as dFADs)32, recovery programme structure (for example, collaboration with local fishers, NGOs and/or nation-states; use of support vessels, and/or chartering of dFAD recovery vessels) and funding sources (for example, reuse of recovered tracking buoys or dFAD plastic floats, and/or polluter-payer systems collected at dFAD deployment or manufacturing) need to be optimized to recover a maximum number of dFADs while minimizing costs and fishing impacts. These considerations highlight the importance of identifying areas leading to losses and multiple ports of different sizes from which operations could potentially be conducted, as we have done above, as well as careful analysis of the possible impediments to implementation of recovery programmes.Some possible impediments to dFAD recovery programmes are environmental, strategic or geopolitical. For instance, although the Somali coast is identified as a dFADs stranding hotspot in winter5 and has potential for a port-based recovery programme as we show here, recovering dFADs along this coast is unlikely to be a priority due to the area’s relatively limited number of sensitive habitats, such as coral reefs, and because of the difficult and dangerous socio-political situation in the country and its adjacent waters. On the other hand, the Maldives archipelago is likely to be a priority given that it is an area with high dFAD stranding rates on coral reefs5 and also has many dFADs that leave fishing grounds and never return. Implementing a recovery programme in this area could be particularly valuable, especially given that the Maldives is well integrated into regional maritime transport and tuna fisheries. However, implementing such a programme for a large island chain composed of >1,000 individual islands will probably be complex. Extensive collaboration with regional stakeholders, such as research institutes, fisher associations and NGOs, as well as buoy manufacturers, would be essential to operationalize a recovery programme in the Maldives and elsewhere.Another major challenge for at-sea dFAD recovery is availability of appropriate vessels to remove dFADs from the water. The vertical subsurface structure of dFADs generally stretches from 50 to 80 m below the surface. The weight of the materials used to build dFADs and the numerous sessile organisms that attach to the ‘dFAD tail’ eventually make dFADs very heavy (up to hundreds of kilograms) and therefore difficult to remove from the water. Complete removal is probably only possible for medium to large vessels with an appropriate crane or winch for hauling heavy material. Purse-seine vessels themselves could participate in dFAD recovery efforts, but this would be costly and disruptive to fishing. For smaller vessels, it may only be possible to remove some parts of the dFAD, potentially aided by natural breakdown of the object or acoustic release systems, such as the GPS buoy, plastic flotation devices and/or surface raft metallic or plastic structural elements. However, this could still be extremely useful as the remaining material will normally sink before reaching coastal environments, thereby potentially avoiding the most important environmental impacts. This strategy would be particularly valuable if the subsurface structure can be made of biodegradable materials9,23,38. Imzilen et al.5 suggested that the removal of GPS buoys by artisanal fishers is already occurring in coastal areas. Therefore, if dFAD tracking information can be made accessible and appropriate incentive mechanisms are put in place to encourage recovery of dFAD elements, this strategy could substantially reduce marine debris from dFADs. Other practical considerations should be taken into account once at port, such as the availability of infrastructure for shipping, disposing of, recycling and/or reusing tracking buoys and other dFAD components. All of these potential impediments can be addressed, but they will require active engagement from fishers, tRFMOs, NGOs and coastal nations.Complementary measuresIn addition to such recovery programmes, existing complementary measures controlling the numbers of dFADs present at sea (for example, limits on the number of operational GPS-tracking buoys and limits on the use of support vessels) may need to be strengthened, as a higher number of dFADs obviously contributes to higher risks of marine debris and stranding. Lowering limits on the number of dFADs may also encourage vessels to increase sharing of buoy information, thereby maximizing use of dFADs and potentially reducing dFAD loss. However, oddly enough, such measures may aggravate problems of ALD dFADs if their consequences are not accurately anticipated. For example, limits on the number of tracked dFADs implemented by tRFMOs have modified the strategy of some components of the purse-seine fishery, encouraging them to remotely deactivate satellite-transmitting GPS-tracking buoys when dFADs leave fishing grounds to maintain the number of operational buoys below authorized limits. The loss of position information prevents the tracking of dFADs outside fishing grounds and may result in under-estimation and spatial bias in estimates of the risks of stranding and loss5,39. A potential solution would be to consider ALD dFADs as part of a stock of ‘recoverable dFADs’ that are not counted as part of the individual vessel’s quota of operational buoys, but for which position information is transmitted and made available to partners involved in recovery programmes39. Other useful options to facilitate the recovery of buoys include limiting the per vessel number of deployments instead of limiting the number of tracked dFADs and/or making new deployments contingent on recovery of an equivalent number of already deployed dFADs. The current tRFMO-implemented reduction in the number of support vessels in the Indian Ocean is also likely to increase the loss of dFADs because these vessels may be used to recover dFADs before they leave fishing grounds, highlighting the urgent need for complementary dFAD management and recovery approaches.Financial considerationsA final question about dFAD recovery programmes is how they could be financed. The logistical challenges described above, such as chartering appropriate recovery vessels, involve substantial costs that cannot be ignored. The most simple and logical financing scheme would be a polluter-payer programme whereby vessels, dFAD manufacturers and/or fishing nations pay some monetary amount per ALD dFAD, potentially in proportion to its expected negative impacts, into an independently run and verified clean-up fund. The basic elements for identifying which vessels, fishing companies and/or nations are deploying dFADs are largely in place via tRFMO reporting requirements, dFAD vessel logbooks and purse-seine observer programmes. The detailed spatio-temporal maps provided here and in Imzilen et al.5 identify where the losses and impacts are occurring, thereby providing a blueprint for apportioning such funds geographically.Missing elementsThe missing elements for reducing dFAD loss are mostly political: facilitating access to tracking and activation-deactivation information for all ALD dFADs (for example, the EU recently objected at the 2nd Indian Ocean Tuna Commission (IOTC) ad hoc working group on dFADs to making dFAD data publicly available for scientific purposes); implementing requirements for appropriate disposal of ALD dFADs; and improving collaboration between industry and regional stakeholders concerned with clean-up programmes. Although these missing elements may seem formidable, there are very promising precedents for rapidly addressing these types of issues. Throughout the 2010s, various initiatives of purse-seine fleets, national scientists, tRFMOs and organizations such as the International Sustainable Seafood Foundation (ISSF) have allowed the rapid adoption of mitigation measures. This was the case for non-entangling dFADs40, best practices guidelines for the release of sensitive species41,42,43, exhaustive observer coverage44,45 and dFAD management plans46, which are all required for ISSF-participating fishing companies if they wish tuna from their fishing vessels to be accepted by ISSF member canneries. A similar approach could be used to address dFAD loss, using the fulcrums of the ISSF, Marine Stewardship Council certification and European Union (EU) environmental regulations to extend the commitments already made by some of the fleets (for example regarding data availability and tests of recovery mechanisms) to other fleets and other areas, and therefore rapidly transform industry behaviour for the benefit of all. More

The Acadian Forest of eastern Canada has shown a pervasive signal of forest degradation since 1985 (Fig. 1). Since 1985, >3 million ha have been clear-cut (Fig. 1d), with most of this area now occupied by either tree plantations and thinnings (Fig. 1c–e), which are dominated by single tree species20, or a mix of early successional tree species (Fig. 1a,d,e). Despite some ingrowth due to succession, old forest has declined by 39% during the period observed (Extended Data Fig. 1a,b; Supplementary Methods). The pattern of extensive harvest of old forest, followed by rapid regeneration of young forest appears to be common across many forest regions of North America (for example, central Canada, southeastern United States, western United States; Fig. 1b) (ref. 10) and can be considered ‘forest degradation’ in that these practices simplify forest structure, reduce tree species diversity and truncate old-forest age classes6. During the same 35-year time period, forest cover remained relatively stable, increasing by a net 6.5% (Fig. 3a, red line)21.Fig. 3: Forest degradation rather than loss drives habitat declines in old forest-associated bird species.a, Habitat trends (1985–2020) for the seven bird species exhibiting the greatest population declines according to SDMs; all of these species are old forest associated. During the same time interval, total forest cover did not decline (red line, right axis), indicating that habitat loss is a function of forest degradation rather than loss. b,c, Predicted habitat loss (pink) and gain (blue) between 1985 and 2020 for two example species: Blackburnian warbler (33% habitat loss; b) and golden-crowned kinglet (38% habitat loss; c). Habitat loss was quantified using SDMs with Landsat data as independent variables strongly predicted population trends for forest bird species.Full size imageOverall, SDMs using Landsat reflectance bands as predictors performed well for most forest bird species when tested on 50% spatially discrete hold-out data (Extended Data Fig. 2; (bar x) area under the curve (AUC) = 0.73 [range: 0.60–0.90]). SDMs therefore provided reliable estimates of habitat suitability and distribution for most of the 54 species. Species with lower model-prediction success tended to be associated with fine-scale forest structure (for example, individual tall trees, standing and fallen dead wood) which are poorly captured by satellite imagery.We back cast SDMs to quantify habitat change for all 54 forest bird species from 1985 to 2020. Habitat declines occurred for 66% of species during 1985–2020; 93% of species exhibited habitat reductions over the past decade (Fig. 3 and Extended Data Fig. 3). Species showing the greatest decreases in habitat were golden-crowned kinglet (Regulus satrapa; −38%) and Blackburnian warbler (Setophaga fusca; −33%; Supplementary Video 1) with seven species showing habitat declines >25% (Fig. 3). Most species with strongly declining habitat are associated with old forests22 (Fig. 4a,b), which is consistent with forest degradation due to harvesting of old forest. Indeed, clear-cut harvest alone was strongly associated with habitat declines for all old forest-associated species (Fig. 4c and Extended Data Figs. 4 and 5). Forest succession into old age classes was apparently insufficient to compensate for this rate of loss. Fifteen species exhibited habitat increases, but most (14 out of 15) of these tend to be associated with young or immature forests (Fig. 4a,b).Fig. 4: Evidence for the effect of forest degradation on mature-forest bird species.a, The relationship between habitat change, estimated from SDMs and independently derived population change estimates from the BBS for the Acadian forest. Bird species of mature (old) forests (M; dark green dots) exhibit the greatest habitat loss; this is generally reflected in strongly negative population trends. Bird species associated with regenerating forest (R; red dots) tend to have stable or increasing habitat but still show BBS population declines. b, The relationship between quantitatively derived estimates of mature-forest association and habitat change from 1985 to 2020. Mature forest-associated species tend to be losing the most habitat in relation to immature- (I; light-green dots) and regeneration-associated species. Successional stage categorizations (R, I, M) are from Birds of the World (BOW). The regression line was fit using a hierarchical Bayesian model (Supplementary Methods) and grey shading in b shows 95% credible intervals. Only a subset of species is shown in b (those with quantitative data for mature-forest associations; Supplementary Methods). c, The relationship between area clear-cut occurring from 1985 to 2020 in each species’ habitat within a 200 m-diameter buffer surrounding BBS routes (N = 90) and habitat loss (1985–2020) at the same scale for six mature forest-associated species. Black lines are regression lines and grey bands are 95% confidence intervals (regression estimates in Supplementary Table 3). As expected, clear-cutting is strongly associated with habitat loss, which indicates that ingrowth of new habitat is rarely compensated for by habitat loss (a signature of forest degradation via old age–class truncation).Full size imageSeveral lines of evidence support forest management as the primary driver of forest degradation rather than alternative mechanisms (for example, climate-mediated forest decline, natural disturbance, permanent deforestation). First, our SDMs did not include climate data so the reflectance changes from satellite imagery used in our SDMs were predominantly due to forest compositional changes. Although climate (for example, inter-annual differences in precipitation) can cause subtle differences in reflectance (leaf colour) over time, most changes in the magnitude of reflectance are due to changes in forest composition or cover rather than effects of climate23 (Supplementary Figs. 1 and 2). Indeed, if the observed habitat declines were due to climate effects or natural disturbance, we would expect to see parallel habitat declines in protected areas, which we did not (Extended Data Figs. 6 and 7). Second, species exhibiting the greatest declines in habitat are those most strongly associated with old forest (Fig. 4a,b), which is the primary target of timber harvest. Indeed, the amount of area clear-cut was strongly associated with habitat loss for old forest-associated bird species (Fig. 4c and Extended Data Figs. 4 and 5). Third, deforestation (defined as permanent conversion to another land-cover type)24 was not a primary driver of habitat loss in our region; deforestation contributed 0.95, and 20 species had posterior probabilities >0.8. Importantly, most of the species showing an effect of habitat loss along routes on changes in population decline have lost substantial habitat over the time period and are associated with old forest (for example, Blackburnian warbler, northern parula [Setophaga americana], red-breasted nuthatch [Sitta canadensis], boreal chickadee [Poecile hudsonicus], dark-eyed junco [Junco hyemalis]; Extended Data Fig. 8), which would be expected with the harvest of old forest—a component of forest degradation. It is important to note that this test is highly challenging because many factors can drive annual fluctuations in bird abundance (for example, weather, phenology, conditions during migration or on the wintering grounds). Also, in any given year, habitat change along BBS routes can be quite small for some species; this low inter-annual variation in a predictor variable can preclude high statistical power to detect effects.We estimated the net number of breeding individuals that have probably disappeared due to habitat loss from 1985 to 2020 using published accounts of territory sizes for each species22 (Supplementary Table 5). This calculation assumes that available habitat is consistently occupied, which is supported by strong associations between habitat amount along BBS routes and bird abundance over the long term. Across all species, back-cast SDMs indicate that a net 28,215,247 ha (282,153 km2) of habitat has been lost, equating to a loss of between 16,779,704 and 52,243,938 breeding pairs (33,559,408–104,487,876 individuals; Supplementary Methods and Supplementary Table 5). One might expect that forest degradation, rather than resulting in broad-scale declines across species, is simply causing species turnover from old forest-associated bird species to young-forest associates. However, it is important to note that we quantified net bird decline from an unbiased list of the 54 most common forest bird species in eastern Canada. This list included both early and late successional species. Such net bird declines could be due to the fact that (1) even some early seral species are losing habitat (probably due to conversion from diverse early successional forest to species-poor plantations and thinnings)26 and (2) in this region, more species occupy older forests than regenerating forests27.We also quantified overall population trends for 54 species of forest birds using data from the BBS (Fig. 6). These estimates give the total magnitude of population changes which include, but are not limited to, habitat loss or gain effects. Thirty-nine of the 54 species examined (72%) are in population decline (defined as having 95% credible intervals that do not bound zero). The magnitude of the declines for 15 forest bird species is severe ( >5% per year). It is notable that most species exhibiting both habitat loss and population declines are old-forest associates (Fig. 4a; bottom left quadrant, dark green dots), with old-forest species exhibiting the greatest habitat losses (Fig. 4b and Supplementary Methods; hierarchical regression, (hat beta) = −16.66 [6.32 SE]).Fig. 6: Population trends for forest-associated birds in eastern Canada.a, Population trend parameter estimates and posterior distributions for 54 species of forest birds derived from Bayesian models. Seventy-two percent of species that are sufficiently common to model experienced population declines from 1985 to 2019. Colour key is provided in Fig. 5. The vertical green line indicates a population trend of zero. Dashed vertical lines coincide with trends of −15% (−0.15), −10% (−0.10) and −5% (−0.05) annual population trends. b, Predicted linear population trends for 1985–2019 (regression lines are mean trends derived from Bayesian Poisson models, Supplementary Methods) including annual variation estimated from BBS data. Shaded purple areas reflect 95% credible intervals and reflect the magnitude of species population declines shown in a. Populations of these eight old forest-associated species have declined 60–90% over the period observed.Full size imageBBS declines are not restricted to old-forest species; several species in rapid population decline are early seral species (for example, Lincoln’s sparrow [Melospiza lincolnii], mourning warbler [Geothlypis philadelphia]; Fig. 4a, bottom right quadrant). Despite the fact that these species have gained habitat over 35 years, their populations continue to decline. Only three species (black-capped chickadee [Poecile atricapillus], hairy woodpecker [Leuconotopicus villosus] and ruby-throated hummingbird [Archilochus colubris]) are increasing in abundance. Populations of these species increased despite evidence of habitat decline (Fig. 4a, top left quadrant)—perhaps because each benefit from anthropogenic habitats and supplemental food. Importantly, habitat changes from 1985 to 2019 along BBS routes were representative of changes at the scale of the entire region for most species (Extended Data Fig. 9), so BBS population trends are highly likely to reflect population trends at the regional scale. This contrasts to the 1965–1985 period when mature-forest loss along routes was slower than in the broader region28.We also modelled BBS population trends over the past ten years, as this is the period of importance for informing listing decisions under the Committee on the Status of Endangered Wildlife in Canada (COSEWIC). Nine species have exhibited population declines >30% over ten years (Supplementary Fig. 3), which meets the criterion for consideration as ‘threatened’ under COSEWIC Criterion A (ref. 29). More