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Results agreed closely with Ref.32 when wind speed-specific drag coefficients were introduced that seemed to be reasonable, as the latter lie slightly under the values published by Ref.17 for a stiffer Canary date palm (Phoenix canariensis). Notwithstanding, the model’s outcome on its own would not be a valid contribution to the field of palm biomechanics and risk assessment. Hence, and from here onwards, the following observations are deemed to be crucial:The researcher32 used data on the mechanical properties of green tissue of senile 80–100-year-old coconut palm stems harvested in 2010 in Fiji and Samoa and which were then published in Ref.34. His model predicted that the critical wind speed for failure of the stem fibres was 82.8 km/h (23 m/s). However, for instance, the cyclone “Val” hit Samoa with wind speeds up to 140 knots (259.28 km/h) in 199135. The real palms from Ref.34 that served for Ref.32 must have withstood, along their life span of 80–100 years, many times wind speeds that exceeded the theoretical “critical wind speed” of 82.8 km/h as predicted by Ref.32. And yet, those palms still stood upright when they were harvested. And no mechanically-damaged tissue in the harvested cocostems was reported in Refs.[32, 34], which suggests that those coconut palms had not suffered any failure of their stem fibres, not even when wind speeds up to 259.28 km/h hit the island they were growing on. This observation adds up to others (e.g.10,19,20,33) that suggest that the engineering approach as used by Ref.32 and used, albeit much simpler, in e.g. the “tree-statics” of Ref.3 may have a limited predictive value.Next: Lowest FI was calculated here at a height of 13.1 m up the stem, whereas32 predicted failure between 6 and 10 m at 23 m/s (while the failure area would move towards the stem base with higher wind speeds). The reason behind this disagreement is simple: the herein employed model uses simple beam theory, which assumes the deformation or curvature of the stem is null. And32, on the other hand, depicted the bending over of the slender palm with a highly-pronounced curvature in the lower half of the stem, which would, hence, cause greater stresses. And as this happens, the upper half of the stem would align itself more with the wind and thus be less deformed, while also the experienced stresses would be lower there (see the figure in Ref.32 p. 126).Furthermore, the present model “fabricated” the rising stresses, by modeling the stem as a hollow wind turbine tower with a changing t/R related to wind speed. The rationale behind this approach is the following: the beam theory neglects that, in palm stems, stresses increase exponentially from the neutral axis and that non-linear deformations and strong curvatures can be experienced by slender palms in high winds, as reported by e.g.23,32. This is thus one of the arguments against the simple beam theory in slender trees and palms, and which has been used in the present model and by e.g.1,3,4,5,16,17,18. One of the premises of that theory is that deformation of the beam should be small. And if, for instance, a slender Mexican Fan Palm bends over, the curvature of the stem could be too pronounced to be faithfully modeled with that theory. And real stresses due to that curvature would hence be higher than predicted. For instance, a company markets commercial pulling test on palms and shows it application on a 22-m-tall and leaning Mexican Fan Palms (La Aduana, Málaga)36. And theirs may thus be an untenable and risky suggestion. A crucial observation can be made here: if regular beam theory (as used in commercial pulling tests and wind load analysis software packages) had been blindly used for the coconut palm simulation (i.e. assuming a stiff and solid beam with no sharp rise in stresses due to the pronounced curvature of the stem and the bending stress increasing linearly from the neutral axis as if the cross-section were both full and isotropic), breaking safety factors would have been greatly overestimated (nearly doubled at 60 m/s) as stresses would have been underestimated. Which could lead to dangerous, and deadly, situations in real-life palm risk assessments.Next: As mentioned in Ref.20 the results of Ref.32 were not validated experimentally. Hence, and even barring structural defects such as cracks or decay, it is a theoretical model that may not allow for accurate safety predictions yet. Furthermore, palm stems were reported to break either at mid-stem or just below the crown37. Whereas32 predicted failure well below mid-stem, extending towards the bottom of the stem at higher wind speeds and so, his results do not agree with real failures as reported by Ref.37. Also, the Red Palm Weevil (Rhynchophorus ferrugineus) has been said to affect the structural stability of palm trunks by excavating tunnels which could lead to their collapse38. The loss of structural integrity due to this tunneling is a type of defect that is not taken into account in Ref.32 either. And Ref.39 suggested several factors that may influence breakage of coconut palms subject to cyclones, and not all are taken into account in the simulation: e.g. the ratio between diameter of the bole and stem height, different mechanical failure modes related to certain hybrids (e.g. the of Malayan Red Dwarf versus Tall Palms), biomechanical degradation due to Phytophthora palmivora and crown characteristics (weight and volume of fronds and crop). The latter also reported fracture of the bole at the root-soil plate level and below ground, while other palms were left leaning after partial uprooting and others had their stems broken at different heights39. This suggests that the predictive value of the approach used by Ref.32 is rather limited and that, even though his is an impressive and highly-valuable contribution, it may not be extrapolable to real-life palm risk assessments yet.Next, the commercial pulling tests and wind load analysis software of Refs.4,5 have been used on palms in Spain. Nevertheless, no clear references pointing towards scientific, peer-reviewed papers of theirs could be found in those publications, relating to scientifically sound data and scientifically contrasted procedures that would support their methods. Which, surprisingly, stands in sharp contrast with their criticisms toward a competitor in business: that the latter would offer (similar) methods without adducing supporting scientific evidence4,5. Also an influential publication asserted with a “generalised tipping curve” that the critical threshold of tilt angle for uprooting would be 2.5° (assertedly based on 400 trees), which seems to be the foundation of their commercial pulling tests3. That strong claim was later questioned as it still seemed unproven and thus hypothetical19. Moreover, the asserted results of3 stand in sharp contrast with those of recent researchers who, after two million measurements on more than 8000 trees, do not assert to have found any critical threshold of tilt angle yet40. Pulling tests have been seriously questioned of late and it is not clear to what extent their predictive value in dicotyledonous trees is reliable or not19,20. Which makes their use for monocotyledonous palms even more questionable as the latter are biomechanically very different from trees. Pulling tests were developed from pulling over dicotyledonous trees, with their corresponding characteristics (regarding material and geometry) of their root system and stem base. However, and on the other hand, the fleshy palm roots tolerate significant bending and twisting before they undergo mechanical failure41. And the roots also sprout from the stembase in a way that is similar to onions. Moreover, the effects of organic exudates from the roots on the aggregation of soil particles, which would thereby create a cement-like soil consistency, was reported too41. And these exudates have seemingly not been reported for dicots yet. And, thus, recommending pulling tests for palms seems to stand for blindly extrapolating hypothetical (as seemingly yet unproven) values for dicotyledons, into the unknown and unexplored realm of palms, and this is could thus be deemed very questionable.Next, destructive experiments were experimentally carried out with the pulling test method of Refs.3,45 on a hollow date palm (Paseo Marítimo, Mataró, Spain) that presented a thin residual wall and a large longitudinal opening42. The palm was pulled with a fixed maximum load of 1.5 kN while performing 20 consecutive measurements with a strain gauge sensor or “elastometer”. The measuring tools (elastometer, inclinometer) had been provided by Refs.3,45. The distance between the two pins of the strain gauge sensor was 2000 mm. The direction of the pull was perpendicular to the opening of the cavity. The first 18 measurements were carried out by placing the sensor aligned with the stem (longitudinally) and in the direction of the pull, conform to the classic pulling test procedure. The highest longitudinal deformation (aligned with the stem) recorded was 0.089 mm (at 2 m height) and 0.045 mm at the height of the cavity (1 m) with a static pulling load of 1.5 kN and conform to the classic pulling test procedure of Ref.3. However, two alternative measurements were made afterwards by placing the sensor first in an angle of 90° (horizontally and bridging the cavity opening) and then in an angle of approximately 30° over the open cavity (Fig. 1), to assess if there could be any shear. Then, the strain gauge sensor recorded the astonishing value of 0.321 mm at the same pulling load (1.5 kN) when placed obliquely (30°) over the open cavity. Which is more than seven-fold the maximum axial strain measured at the same height of the stem and aligned with the pull, which indicates that shear (and not longitudinal strain) was the highest. The two sides of the open cavity seemed to slide over each other. This phenomenon can be visualised by bending a softcover book: the pages slide over eachother. Palm wood is much weaker regarding shear stresses and when this is coupled with extraordinary shear deformation (due e.g. a large, open cavity such as here) failure of the hollow stem can be triggered at lower loads than predicted by the beam theory. And if this extremely hollow stem exhibited high shear deformations under a transverse pull, then logic says that also other structural behaviours (e.g. ovalization, cracking or kinking) could set in.Figure 1The strain gauge sensor was placed obliquely over an open cavity in a date palm, recording high shear deformations. The red arrows show the direction of the strain, producing shear as the two halves of the stem seemed to slide over each other, while a static pull was being applied.Full size imageTo continue: the death of a man crushed by a Canary date palm in 2020 in Barcelona (Spain), triggered the implementation of drilling with the Resistograph of Ref.1 and “oscillation tests” on 2026 date palms with a “reliability of almost a 100%”, carried out by Refs.36,43. Nevertheless, it was stated that drilling cannot predict the residual strength of a structurally damaged trunk44,45. And it was shown that boring into decayed zones would probably augment the speed at which decay spreads46. Micro-drilling would allow fungi to grow out radially due to the microenvironment the narrow channel creates47. And drilling was also questioned by Ref.48. And correct assessments would rely on comparing results with both known standards and decay-free cores taken from the same tree, which would make this method thus highly-invasive49. Hence, (micro)drilling is currently highly questionable. And it was reported that the “oscillation tests” of that company were as follows: the palm is pulled manually with a rope and if the whole stem moves the palm stem is regarded as sound50. This was the only description found of those “oscillation tests” on palms after exhaustive online literature review. That company recommends that their oscillation test be used in all palm risk assessments36. Nevertheless, no clear references can be found in Refs.36,43 that would point to peer-reviewed publications in scientific journals that would validate their claim, so it is not clear if it is scientifically tenable or not. However, their claim could be interesting, if robust and supporting scientific evidence could be adduced and if their procedure were unbiased, documentable and reproducible by a third party.Next, the famous “70%” criterion was adduced by the Municipal Manager of the City Council of Barcelona after the deadly accident with a Canary date palm and the manager was reported to have said that inner decay was considered problematic if at least 70% of the stem diameter was affected by it12. And that the risk of breaking would gain importance if the extent of decay occupied at least 70% of the stem radius13. However, if the palm collapsed with only 25% of its radius affected by decay, should one then not immediately refute the “70%” criterion? Notwithstanding, the applicability of this criterion as suggested for palms by Ref.1, can easily be refuted scientifically too: Firstly, suspicions of falsification were published regarding that highly-influential Visual Tree Assessment (VTA) rule t/R = 0.32 or 70%51. That t/R rule for risk assessments defined the (supposedly) allowed degree of hollowness of a tree trunk and is still being used world-wide, although it is allegedly the result of falsification51. Secondly, that famous “70% criterion” was, moreover, developed for dicotyledonous trees and not for monocotyledonous palms. Thirdly, tangential MOR (tension perpendicular to grain) in coconut wood was found to be as low as 0.233 kN/cm2 whereas longitudinal MOR could reach 5.22 kN/cm252. And shear strength of date palm wood could be as low as 7.14% compared with its longitudinal MOR52. Even longitudinal MOR varied greatly between coconut, oil and date palm52. For oil palm (Elaeis guineensis), the proportion of tensile strength perpendicular to grain to longitudinal MOR was reported to be only 2.08%53. And another researcher found that tangential MOR would be 36.77% approximately of longitudinal MOR in coconut palms32. Therefore, tangential cracking followed by longitudinal splitting in straight hollow stems would thus be triggered earlier in date and oil palms than in coconut palms. Palm wood is highly anisotropic compared to dicotyledonous hardwood trees and, above all, there are great differences both among different palm species and even within the same palm species, depending on e.g. age and growth. And, hence, a fixed t/R rule seems to be an untenable recommendation for palms. And this would even not be applicable in stems with side openings or those with only heart decay30. Or with irregularly-distributed pockets of decay combined with cracks or invaginations. Even if one dared to leave aside the fact that high stresses can be caused by strong curvatures of slender palms in high winds (see e.g.23,32) and those are not taken into account in the VTA t/R rule of 70%. Cross-sectional flattening or ovalization, leading to cracking of thin-walled hollow stems, is neglected by classic beam theory too, while those structural failures depend highly on the MOE of the wood54. And MOE, MOR and density vary greatly according to species, age, et cetera and thus preclude a fixed t/R rule entirely from being useful when the aim is to predict the structural collapse of (especially) palms.Next: Scientists published breaking safety factors and critical wind speeds for severely decayed date palms in a highly-frequented area in a major Spanish city16. Their suggestion that those decayed palms would withstand wind speeds of at least 135 km/h even made headlines55. They did not fully explain the underlying methodology in their online-published report. However, the manual of the acoustic tomograph (Fakopp56) they used, suggests that breaking safety factors and critical wind speeds were calculated by means of simple beam theory, which is based on longitudinal stress and strength, and assumes that wood is a homogeneous material (i.e. isotropy). However, palm wood is highly anisotropic, meaning that shear, delamination, torsional, radial and tangential tensile stresses could cause structural collapse, especially in decayed palm stems, even if the beam theory were applicable. So, their claim that decayed palm stems in a market square would withstand hurricane-type winds, is possibly based on what looks like a methodological error on their behalf. On the other hand, a method for the breaking risk assessment of Canary date palms was offered in a scientific paper through beam theory and a hypothetical static wind load17. But, fortunately, the latter acknowledged that their approach could not be valid for decayed stem areas due to shear and that failure due to progressive fatigue was not considered either. And a highly-cited researcher calculated longitudinal stress in a Mexican fan palm (Washingtonia robusta), seemingly based on the assumption that bending stress would increase linearly from the neutral axis and by means of a static pulling test combined with simple beam theory18. However, the herein offered remarks suggests that the latter’s approach for palms may be a simplification that has room for improvement too. A Spanish researcher published results from static bending tests of planks, sawn from a Canary date palm, to be used in the context of mechanistic risk assessment models57. Unfortunately, the procedure he used precludes his results from being useful in that context, as current models need MOR and MOE values obtained from compressive axial and tangential tensile tests. Nevertheless, in his review he rightly acknowledged the dubious efficiency of published risk assessment models, as they would depend too much on a wide palette of unknown variables (e.g. Cd, MOE, MOR, density) and that decay detecting tools were not efficient, as reference values did not exist (e.g. to calculate strength loss in comparison with sound palm wood)57.Next: Some readers will surely feel tempted to use the model for, and extrapolate the results to, commercial palm risk assessments. But that would clearly be premature, as can be inferred from the following observations: a wind-speed-specific drag coefficient was proposed herein to simulate the coconut palm of Ref.32. But a sturdy stem (in contrast with the studied 25-m-tall and flexible stem) would need to be modeled with a different Cd. And to make Cd transferrable to other palms, non-linear deformation would have to be taken into account. This non-linearity is the result of the slenderness, anisotropy and geometry of the stem, the flexibility of the crown and overall out-of-phase damping. A greater curvature of the stem leads to higher three-dimensional stresses (longitudinal, radial and tangential). And Poisson’s ratios also determine deformation of the fibres32 and this may differ too. And all of this clearly exceeds the capacities and predictive power of simple beam theory. Also, wind drag has commonly been estimated as being proportional to the square of the wind speed. However, it was shown that this estimation may be too high for flexible culms, as at higher wind speeds the drag would be linearly proportional58. And, hence, real loads would be lower than predicted. They also found that the risk of mechanical damage was comparatively lower at higher wind speeds, as the plants’ height was reduced by up to 45%58. It was suggested that coconut palms would resist hurricanes better than dicotyledonous trees because of the same strategy32. On the other hand, common sense and observation suggest that the mass of palms (stem, crown and crop) combined with violent gusts may lead to dynamic loading that far exceeds predictions that take into account static loading only. Two simulation studies did not consider dynamic loading, damping, looping or inertia effects20,32. Nevertheless, the results the first agreed very well with commercial softwares that, assertedly, would include dynamics and natural frequencies20. For instance, it was asserted that “and statics integrated methods that combine static pulling with dynamic wind load assessment (Wessolly 1991; Brudi and van Wassenaer 2002; Detter and Rust 2013)” (sic)59. Related authors also suggested that a natural frequency factor was incorporated in their calculation of the wind load and bending/uprooting moment of their pulling tests3. However, robust scientific evidence that would support their claim was not found and neither did the mathematical simulations find any evidence of dynamics20. Not including the influence of dynamics (e.g. the swinging of slender trees and palms) in a wind load analysis could underestimate real wind-induced loads. Which means that the palm or tree could thus fall down even if it had been assessed as “safe”. Also the weight of crop (e.g. dates or coconuts) could add inertial forces to the swinging and this could be a subject for future research on wind loads in palms20.Further: Mechanical properties (strength, stiffness and density) of green palm tissue are still a relatively unexplored field, although several palm species have been studied17,22,32,52,53,60,61,62. Properties from these publications of other palm species could be introduced in the model to explore their importance relative to other influencing factors such as slenderness, wind speeds, loads, et cetera. But, and even though this procedure has led to good agreement for FI of Ref.32, more research on the applicability of the model should be carried out.Also: The herein used approach is based on a simplified version of the theory of elasticity, which ignores stress concentrations (e.g. around knurls or defects in wood), Inglis’ potential energies, fatigue and crack propagations as described by Ref.63, which can lead to unexpected structural collapse if one relies solely on simple beam theory. The need to explore those ideas was suggested, as understanding their influence could be the key to understanding the relationship between structural failure and wind42. Fortunately, those relatively unexplored ideas were later applied to calculate critical wind speeds for failure in forest trees64. This could thus be an interesting starting point for research on the breaking prediction of palms and trees.Moreover, the beam theory as used by some of the herein mentioned authors is aimed only at predicting conventional bending failure (axial compression stress that exceeds MOR), while low t/R ratios can lead to Brazier buckling or tangential cracking followed by longitudinal splitting in hollow stems30. The formulations were offered to predict the bending moment at which cracking failure would occur in hollow trees, based on t/R, MOE and tangential tensile MOR54. So, and for instance, if one took an oil palm and a coconut palm, both hollow and with an identical t/R and wind loading, the first would crack earlier than the second due to a lower tangential MOR. And as t/R decreased, failure modes would be bending failure, cracking and Brazier buckling respectively, for oil palm. Whereas in coconut palm, and depending on t/R, bending failure would occur earlier than the other two modes due to a comparatively higher tangential MOR/longitudinal MOR proportion. Which is also evidence why fixed t/R rules (e.g. 0.32 or 70% of the radius) and beam theory (e.g. pulling tests) cannot be applicable to palms. Hence, incorporating cracking and buckling predictions in the assessment of decayed and concentrically hollow palm stems, could also be an interesting lead.Next, it was found that the Brazier calculations (based on MOE) agreed with the BS outputs (based on MOR) of the model for all heights along the stem and all wind speeds, when the stem was modeled as untapered, which is interesting. The relationships of varying MOR and MOE along the stem were based on the measured densities of Ref.32, so there seems to be a consistent mathematical relationship between the MOR, MOE and density values32. At first sight, densities of green palm wood could thus be an interesting future research subject. However, and on the other hand, it was reported that no correlation existed between density and mechanical properties in date palm wood52. Which would make future mechanistic modeling thus even more challenging.Next: None of the herein investigated models and criteria fulfill the requirements (i.e. that models should account for cell wall expansion and sclerification as a result of height growth and age) stated by Ref.60. And they could therefore be precluded from being useful for palms, as the latter both grow and age.Researchers also recorded longitudinal tensile stress on the surface of upright growing trunks, whereas compression stress was found at the bent area of leaning trunks in coconut palms due to growth stresses65. They also found compression stress in the outermost portion of the inner cylinder of the coconut stems, which they said was radically different from dicotyledonous and coniferous trees. So, this also questions the application of fixed t/R rules, pulling tests or wind load analysis combined with beam theory on palms, as those methods neglect growth stresses and their biomechanical importance. For instance, if the central cylinder were missing (e.g. due to butt rot caused by Ganoderma zonatum) then the lack of those inner and outer growth stresses and strains should be accounted for.However, now we will suppose, and for the sake of the argument, that we approached the pitfall in which some of the aforementioned companies and researchers have seemingly already fallen. At a first glance, it would be appealing to suggest the following method: consider that commercial software packages for wind load and breakage predictions were successfully simulated20. And that special software packages were also suggested to accurately measure the vertical area of e.g. a palm crown, which would thus allow to perform a wind load estimation that would meet the standards of the commercial software packages investigated20. Suppose a wind speed-specific drag factor be introduced, such as proposed for Canary date palms by Ref.17 or the one found here for coconut palm. And that the formulations for the critical bending moments for tangential cracking from Ref.54 and the ones employed in this study for pure bending failure be incorporated, together with the wood properties as published for several palm species by e.g.17,22,32,52,53,60,61,62. Furthermore, values for peripheral material properties were obtained from the ring that corresponds to the outer third of the radius32,52. And take a non-linear bending stress distribution in the cross-section of the stem, which rises exponentially from the neutral fibre to the peripheral outer ring made of the most dense, stiffest and strongest tissues21. Then, a simplified assumption would be to calculate stresses taking into account only the outer third of the radius (i.e. t/R = 0.33), as if it were a hollow wind turbine tower and as has been done in the present study. And this, to simulate (in an extremely simplified manner) non-linear bending stress and peripheral material properties (note: it should absolutely be stressed here that this is not regarded as a validation of the VTA t/R = 0.32 rule, as the rationale for its use in the model differ from the rationale of Refs.1,15, while the inapplicability of the latter’s claim has been amply evidenced in this study). In this way, theoretical safety factors could then be calculated and compared for bending versus cracking failures of the hollow palm stem, for varying wind speeds and several palm species. This would thus be similar to the widely-cited Statics Integrated Assessment (SIA) and Statics Integrated Methods (SIM) of Refs.3,45, but then for palms and slightly enhanced (as it adopts cracking failure, the varying material properties across and along the stem and a wind speed-specific drag factor). And as less advanced methods (e.g.1,3,16 have already been commercially marketed, a non-scholar could perhaps be tempted to commercialise this model in a software package or use it for their consultancy services too. However, this approach would still suffer from the same limitations as described in Refs.19,20 and in the present study. And it would still be theoretical, as the variables concerning the structural stability of hollow trees and palms may be too diverse to be assessed with current methods19. And the combination of small deviations in the real palms from e.g. the published values for MOR and MOE and theoretical drag factors (and hence predicted wind loads) could result in a global deviation that may invalidate the outcomes (the latter concerns all of the herein investigated methods too)33. Hence, and even though it is not the corresponding author’s idea to wholly negate the usefulness of the herein investigated methods, it is crucial to point out that both their validation and predictive value are seemingly problematic.A crucial rationale for presenting the utterly simplistic model in this paper was the following: supposedly complex models such as e.g.3,4,5,45 or the advanced 3DFE simulation of Ref.32 may obscure that fact that those models can be as tied to the same limitations as the simple model presented herein. And apparently complex equations (or e.g. a high number of citations of the related papers) may deviate the readers from the fact that factual empirical and scientific evidence could still be missing that would validate the models for real-life purposes. Hence, a simple model such as the herein presented one, may serve the purpose of pointing out the flaws and limitations of the seemingly more advanced models, while it even seems capable of simulating internationally-renown commercial software and methods20.So, the time seems to be ripe now to go beyond the classical procedures as trusted upon by the arboricultural community so far (and discussed before).Even in straight, thin and idealised cantilever beams, bending–torsion coupling deformations can arise due to the dissimilar bending stiffnesses when the two planes (horizontal/vertical) of the cross-section are of uneven dimensions (instead of a e.g. a perfectly circular or annular cross-section)66. Palm and tree stems are not always perfectly round due to dissimilar diameters in the horizontal versus the vertical plane (e.g. in cases of reaction wood, open cavities or uneven radial growth due to touching physical obstacles). Hence, simple beam theory (e.g. pulling tests) may thus not account for torsional (and, ultimately, catastrophic) behaviours, even in straight stems. Moreover, if improperly applied, simple beam theory may theoretically predict the strength and stiffness requirements of a structure to be satisfying, while unforeseen collapse may later occur because of the loss of stability (buckling), including intriguing phenomena such as non-linear geometric deformation and wrinkles66. Translated into arboricultural language: the tree or palm that had been assessed as “safe”, suddenly collapses unexpectedly. Therefore the need in this paper to show the arboricultural community that structural collapses, that have been studied for centuries in other fields such as mechanics and engineering, should not be ignored.The risk of buckling of a Mexican Fan Palm (Washingtonia robusta), assessed by the corresponding author in 2003 in the Atocha train station (Madrid, Spain), gave birth to a proposal to assess the risk of Euler buckling while carrying out wind load estimations in order to optimise artificial supports (e.g. cabling of the palm to nearby structures)28. Prior to the assessment of the last standing palm, several other slender Mexican fan palms in that train station had already collapsed, even though the interior of this giant greenhouse is free of wind loads (Fig. 2). The photograph is a testimony to a rather neglected fact in commercial arboricultural methods: structural collapse in absence of wind loading and pure post-buckling failure. In this case it was hypothesized that these palms had initially become elastically unstable, by exceeding their critical stem height and weight. It was hypothesized, too, that this had been caused by their unlimited growth towards the glass ceiling searching for light, the absence of external loading stimuli such as wind (the lack of which would have made the palms not to invest in stiffer and denser wood) and optimum growing conditions (permanent moisture and warmth). The weight of the crown, small horizontal displacements, watering from the ceiling (i.e. fog to keep the atmosphere moist) and resulting gravity forces would then have further influenced the failure process, leading to final collapse. This example illustrates how plants can adapt to their environment and that biomechanical failure can be possible in total absence of wind loading. In large-wave Euler buckling, the column curves and deviates laterally to escape from compressive loads (such as e.g. self-weight) before axial stresses surpass axial MOR. The column becomes elastically unstable and buckles under its own weight. The critical weight divided by self-weight gives the safety factor and only when this safety factor is higher than unity can columns, or plants, bear additional loads such as wind, snow or ice. The critical buckling height or weight is a function of stem height, diameter, tapering, MOE, density of the wood and loading conditions27. The latter also showed that buckling safety can be overestimated if the stem is improperly assumed to be untapered, cylindrical, free of imperfections and isotropic27. They also offered an overview of why predictions of structural collapse may easily differ from real-life situations27. Moreover, the bifurcation point is the sudden jumping process of a beam from a straight-line to a bent shape, causing instability or buckling66. Pre-buckling analysis has proven to be rather straightforward for a simple pole, while the post-buckling process that describes the finite deformation of the structure (which may lead to its collapse after damage and faults accumulate to a certain value) requires a large set of numerical solutions67. Strong geometric nonlinearities and large displacements of the post-buckling behavior of a slender rod were studied, leading to a quantitative calculation of the post-buckling deflections of a hollow oil sucker rod67. Translated into the world of palm biomechanics, it means that: while pre-buckling of the stem would already be a daunting task due to the varying taper and MOE, predicting its post-buckling behaviour and final collapse (including structural faults such as e.g. cracks or pockets of rot) seems to be out of reach, as palm stems are not human-made structures. And yet, it seems reasonable not to ignore this type of structural behaviour in future palm risk assessments. It was acknowledged too that Brazier buckling played a crucial role in the local instability of plant stems66. And this was also a reason to include Brazier buckling of a hollow wind turbine tower to simulate breaking safeties of the coconut stem in the present paper.Figure 2The slender Mexican fan palm anchored to the ceiling of the Atocha train station, Madrid. The cabling configuration was installed to minimise damage in case of post-buckling collapse. The other palms had collapsed before, even though there are no events of wind inside this giant greenhouse.Full size imageNow, and as a second part of this “Discussion” section, the following reasonings elucidated from literature overview, visual observation and intellectual reasoning, are presented to postulate ideas that may serve to show the way towards a future unified theory on palm risk assessment.First: The biomechanical structure of palms seems to have evolved towards highly-efficient energy dissipation and viscoelastic damping capacities under strong and dynamic wind loading. To achieve this, a triple-helical mesh of tough (high tensile strength) fibrovascular bundles is embedded in a soft parenchymatous foam, which both contribute to damping and energy dissipation32,68. The fibrovascular bundles run along the stem in a screw-like fashion and across the stem in a radial zigzag pattern (this also sets palm wood also apart from dicotyledonous wood, as in the latter the fibres are stiffly glued together and, most importantly, axially aligned). It was asserted that this screw-like pattern can hold the bending stem together under high wind loading as it lends the stem a higher stiffness and strength when the fibrovascular bundle orientations varied between 0° and 9°32. This pattern was also suggested to minimise longitudinal splitting and thus enhance the mechanical efficiency of the stem32. This structure was an inspiration for spirally-laminated hollow veneer-based composite poles32. Also high microfibril angles across the fibre cap would result in a high extensibility of the stiffening tissue, which would enable palms to cope with considerable deformations under wind loads in Mexican fan palms69. Large deformations in bending and torsion under wind loads of the petioles were said to combine efficiently with water and nutrition conduction, due to the optimized connection of their vascular bundles to the leaf traces68, which allows to suppose that also the crown is optimized regarding damping and energy dissipation brought about by dynamic winds. And the contribution of both parenchymatous and vascular tissue of palms to energy dissipation, dynamic response and flexibility, and thereby improving impact resistance, was described too70.Second: Palm wood is highly sensitive to shear, delamination and splitting in comparison with dicotyledons. For instance, when samples were taken by Ref.17 to perform longitudinal compression and tension tests, then this irregular structure of the palm tissues unwillingly led to longitudinal fractures, sliding and shear in the samples, and thus seriously limiting experimental data on axial MOR. And thick disks of coconut wood were manually torn apart, while the delamination followed the helical pattern of the fibrovascular bundles that tangentially deviated across the disc diameter32. Hence, it is thus not unreasonable to suppose that this sensitivity to delamination and shear may lead to the stem’s structural collapse, especially when this helical path of bundles is interrupted by a mechanical defect (e.g. pockets of rot, irregular decay, cracks or tunneling by Red Palm Weevil). This reasoning aligns with another researcher’s too, who likened coconut and oil palm stems to a composite material made of a matrix and reinforced elements and found that shear and tension perpendicular to grain greatly govern the bending behaviour and structural stability of the stem52. The aforementioned “spirally-laminated hollow veneer-based composite poles” suggested by Ref.32 may be very stiff and strong when undamaged (i.e. if this helical pathway of fibrovascular bundles is not interrupted by a mechanical defect and thus a completely defect-free beam). But, an interruption along this path may trigger delamination and splitting along the “veneer”. Crack propagation and splitting could thus follow the helical path of the fibrovascular bundles. And predictions based solely on simple beam theory and axial stress and strain would then be less than acceptably reliable. Observations and experiments that seem to support this hypothesis are e.g. the aforementioned Canary date palm that crushed a man in Barcelona, as a small inner crack was said to have triggered the sturdy stem’s collapse with a breeze of only 38.2 km/h10. Also pulling test experiments carried out in 2004 by the corresponding author showed that the mechanically damaged palm stems under artificial loading started splitting first, leading to full collapse afterwards42. Those experiments (partially published in 200542) had been kindly supported by Josep Selga S.L., the City Councils of Terrassa and Mataró and the Asociación Española de Arboricultura, while the instrumentation had been kindly provided too (Picus tomograph: L. Göcke Argus Electronics; Pulling tests: Brudi and Partner Tree consult and Dr. Ing. L. Wessolly; Resistograph F300, IML: the City Council Terrassa). The aim was to assess whether the pulling tests of Refs.3,45 could be adapted to palms or not and if experimental data for MOE could be obtained from standing palms. Acoustic tomography (Picus tomograph) and microdrilling (Resistograph F300) had also been carried out on several damaged palms, but had not facilitated any reliable breakage prediction either (unpublished results). An example is shown in Fig. 3 where a desert fan palm (Washingtonia filifera) collapsed under a static pull, after slanted longitudinal splitting and delamination was initiated at the border of the open cavity (upwards and downwards)42. Also Fig. 4 shows how delamination (triggered at the height of the open cavity under a static pull) led to total collapse of a date palm stem. No primary axial compression failure was observed macroscopically42. And a hollow date palm exhibited extremely high shear values in comparison with axial deformation at the height of a large, open cavity (Fig. 1)42. Moreover, it is not unreasonable to suppose that if strong, cyclic and repetitive dynamic wind loading had beaten these three palms (instead of a static pull), the risk of structural failure could have been heightened by progressive fatigue of the wood around the structural defects (and thus earlier crack formations/propagations and at lower loads than with the static pull).Figure 3When a decayed desert fan palm stem was statically pulled, collapse was initiated by splitting of the hollow stem. Cracks first appeared above and below the open cavity and initiated at its borders (red arrows) and total collapse only ensued after large longitudinal splitting and delamination.Full size imageFigure 4When a decayed date palm stem was statically pulled, splitting was initiated at the open cavity and total collapse ensued by delamination.Full size imageThird: Highly deformable and soft but elastic materials can exhibit types of structural deformation under mechanical loads that are unlike those commonly observed in elastic structures that behave linearly71. Kinking at the inner side of soft, elastic cylinders was observed after the cylinders had become elastically unstable due to Euler buckling71. The extreme localization of curvature at the compressed inner (not outer) side exceeded a critical value leading to a sharp fold. When the cylinder was kept under a bending load for several minutes, irreversible defects appeared at the location of the inner kink which, in subsequent loading cycles, progressively lowered the cylinder’s structural stability under the same amount of load71. Translated into palm stems, and assuming they are highly deformable, soft and elastic, this means that inner kinks and defects could appear and lead to structural collapse due to fatigue and cyclic loading beyond the critical curvature. Brazier buckling was also observed in soft, elastic and hollow cylinders and the occurrence of either kinking and/or ovalization was found to be dependent on the ratio between the diameter and the wall thickness71. When one envisages palm stems as has been done in the present paper (a viscoelastic cylinder), then the kinking and ovalization of the cylinder (here: the palm stem), after becoming elastically unstable, could thus lead to abrupt structural collapse while not obeying simple beam theory. Calculation of the critical curvature at which buckling sets in was said to be rather straightforward, but the posterior evolution of the kink or defect would need detailed non-linear theory71. Hence, the modeling of elastic pre-buckling (i.e. prior to these aforementioned structural failures) seems to be more within reach for palm stems than post-buckling collapses. No experiments have been performed on palms yet to either confirm or refute these extrapolated suggestions, but the latter are possibly worth considering in future research or risk assessments.Fourth: Developing a mechanical model seems currently out of reach as strength and stiffness (and thus damping) seem to evolve over time in the palm stem as a function of the location of the vascular bundles within the trunk, age (and ensueing additional cell wall layers and (secondary) growth within the trunk) and growth conditions52. Also the lack of a statistical correlation between MOR and MOE and wood density in date palms is, inexplicably, contrary to other investigated palm species, which also obstructs the path towards reliable mechanistic modelization52.Fifth: It was stated that “Reliable prediction of delamination growth is still proving to be problematic” in human-made wood products, whereas simply localising starting points for delamination would possibly be more within reach72. From which it can thus be inferred, that reliable predictions of delamination-triggered collapses of Nature-made palm and tree trunks seem currently to be out of reach. But that would still be no reason to neglect this type of structural failure).Sixth: The existence of silica in palms was mentioned by Ref.52 (p. 158) and studied by e.g.73,74. Researchers concluded from a literature review that the mechanical properties of palms could be enchanced by silica73. And the role of silica in plants was described as: “Biomineralization is a naturally occurring process by which living organisms form skeletons from inorganic minerals such as silica and calcium”75. The latter also found flexural rigidity in rice plant leaves to increase with increasing silica content. It has been suggested by practitioners and arborists in Spain that silica and biomineralization would make the palm stem stiffer and stronger around structurally defective areas, as an alleged reaction to strength loss percieved by the palm itself (i.e. a substitute for compensation or thigmomorphogenesis as studied in dicotyledons), but no scientific findings were found that would support their suggestion.Seventh: Local mechanical performance (i.e. damping and the diminution of stress discontinuities) of a Mexican fan palm stem could be controlled by the plant itself up to a certain point by adaptation69. Which would further complicate the mechanical modeling of structural stability versus (wind) loads.Eighth: The cracking formulation of Ref.54 should unfortunately be precluded from being useful in hollow palms, as their formulation assumes that the fibres are aligned along the tree axis, while palms present a mesh of triple-helical fibrovascular bundles in a screw-like pattern along and across the palm stem.Ninth: Based on visual observation, young and still flexible and soft Mexican fan and windmill (Trachycarpus fortunei) palm stems seem to exhibit a viscoelastic behaviour when manually pushed and pulled. Their moving out-of-phase with the pulls can be felt by hand and feels like a structure made of foam, but with a certain resilience. Their behaviour resembles neither that of a steel spring nor that of foam or a stiff and non-deformable beam. And this in contrast with e.g. flexible dicotyledonous saplings and tree branches that almost behave like springs or lashes when laterally loaded and released by hand. Also visual observation of the damped manner in which older, taller and stiffer Mexican fan, date and windmill (Trachycarpus fortunei) palm stems move out-of-phase in strong winds seems to confirm this. And in several palm species the woven mesh of leaf sheath palm fibres attached to the stem also exhibits a damping and viscoelastic behaviour when manually manipulated. In windmill palm for instance, the stem is wrapped in a burlap-like mesh of brown and coarse leaf sheath fibre, clasped around the trunk. Manual manipulation of that mesh suggests that friction among the fibres could contribute to damping of leaf and stem movements. A review of published findings on damping and energy dissipation in palms seems to confirm these visual obervations too (see32,68,69,70). A viscoelastic structure exhibits a non-linear response to the strain rate, in which cyclic stress is out-of-phase with strain, as some of the stored energy is recovered upon removal of the load, while the remaining energy is dissipated as heat. The modulus is represented by a complex quantity: on the one hand the stiffness is defined by elastic behaviour and, on the other hand, the energy dissipative ability of the material is defined by the material’s viscous behaviour. Hence, one could thus hypothesise that the palm stem could be neither an elastic nor a viscous structure, but a combination of both.So now, the aforementioned observations lead us to the following:The herein postulated model envisages the palm stem as a viscoelastic and hollow cylinder prone to Euler and Brazier buckling and ovalization and kinking. This hypothetical model could graphically be imagined as a hollow foam pool noodle with a triple-helicoidal embedded mesh of tough (a high tensile strength) fibre bundles. Both the foam of the pool noodle and the mesh of fibres contribute to the damping while the latter also adds flexural stiffness under bending. The cylinder exhibites a non-linear response to the strain rate, in which cyclic stress is out-of-phase with strain, which makes the whole structure viscoelastic. This envisaging was the main reason why Eq. (10) for Brazier buckling, with a constant t/R for all wind speeds, was experimentally applied to simulate FI of the cocostem of Ref.32.However, it would also be prone to delamination, splitting and shear as the bundles are glued together with “foam” along their screw-like path. The momentum the cylinder should withstand should be a result of dynamic wind loads, mass and inertia that cause a non-linear deformation and pronounced curvature of the cylinder (non-linear due to the varying material properties along the (tapered) stem and structural damping). Strains in the stem would then not be linearly proportional to the load, by which Hooke’s law (ut tensio sic vis) would not be not applicable. And stress would rise non-linearly along the stem radius from the core to the periphery. Progressive fatigue of the wood, or at structural defects (e.g. crack initiations and progressive propagation due to repeated dynamic wind loading), should be taken into account. This model would now possibly align quite well with the scientific findings cited in this paper.Nevertheless, a simple mind experiment can reveal the additional baffling challenges found in real palms: imagine a date palm trunk that has been severely tunneled by Red Palm Weevil and/or pockets of rot: the structure resembles a piece of Gruyère cheese and allows remaining bundles of sound strands to be torn off by hand, as the stiff vascular bundles are just lightly glued together by means of a foamy parenchymatous tissue. The remaining bundles and volumes of sound wood, bordering the void and decayed spaces, could then resemble irregularly shaped columns. Now, imagine the loading of this disk of “Gruyère cheese” due to a bending moment: an infinite variety of kinds of structural failure would take place within the remaining “columns”: buckling, sliding, shear, sideways kinking of the fibres, torsion, crack propagations along a triple-helical path, stress concentrations, et cetera. And as the smart reader will surely agree to, this three-dimensional failure process is totally impossible to depict, or assess, by means of drilling, tomography or simple beam theory. Doubtful readers can have a look at the Figs. 10 and 11 in Ref.17 and imagine that the wood blocks in those figures were the remaining “columns” of our imagined trunk. And, as it can be seen in those figures, the blocks structurally failed due to shear, even under pure axial compression and tension17. And now let us add the following: looping movements of a tall palm in winds has already been recorded18. These looping and circular motions of the stem, inevitably, cause a rotative loading of the cross-section of that same stem. This rotative motion thus causes compression stress (and tension stress on the opposite side) at the periphery and in a circular motion, Real wind loading of palms is thus very dissimilar to the unidirectional loadings (assumed or performed) by e.g.1,3,16,17,18,32,36. And let us add too, that shearing behaviours can be caused due to structural defects (e.g. see Fig. 1), and couple this with the rotative motion and possible progressive fatigue processes in the root system and stem. Now the abovementioned reasonings leave us with a mind-boggling panorama of infinite variables, which seemingly precludes all herein investigated methods from being reliable. However, and from a constructive point of view, these postulated ideas are possibly the best starting point for the development of a future risk assessment method. And the herein offered observations can be used by arboricultural professionals to enhance their tree and palm risk assessment consultancy reports.This is only a partial theory, which need not cancel out others per sé, but may overlap others so as to reach a more acceptable degree of predictive accuracy. This may be a step toward a more complete, fully-unified and more reliable theory that would enable us to make predictions that agree with observations to an acceptable degree of accuracy. Constructing a complete theory from scratch looks excruciatingly difficult now, so perhaps the way forward would be to overlap existing partial theories. Partial theories describe a limited variety of events while leaving others aside. Current partial theories in arboriculture do not seem to be valid on their own19,20,33. Examples are theories that neglect common mechanical behaviours of the wooden body33, simple beam theory and dubious t/R criteria for palm risk assessments. Or predictions of uprooting and breakage that are based on a static wind load analysis, if the latter does not take into account the influence of slenderness, dynamics, mass and inertia in slender and top-loaded (due to e.g. a lion-tailed crown or heavy crop) palms and trees20. A complete theory would thus contain a number of parameters which values, in real-life, cannot be predicted yet and such values may have to be chosen to fit in through experiment. A very appealing goal would be now to overcome this mind-boggling and infinite combination of behaviours and (structural and material) properties, and distill it all into one simple and generic law/model, as was elegantly done for buckling by Ref.24.Researchers have taken sound stems as a starting point (e.g.17,18,32. But, perhaps structurally-damaged trunks should be the place from where to start, as the latter are generally the aim and goal of risk assessments. Future methods could thus perhaps focus on deformations of the stem under circular (wind or artificial) loading, while three-dimensional mechanical behaviours and failures can reasonably be expected within a damaged stem. And also three-dimensional material properties should be taken into account: i.e. MOR and MOE in all anatomical directions. But, as taking those values from published tables would not be feasible (due to the high variability of those properties), different methods from the ones used by e.g.1,3,5,16,17,18,32,36 should perhaps be devised. For instance, a preliminary investigation was carried out on forced vibrations, and resulting resonance frequency values, for a Mexican fan palm, in the light of the identification of trunk decay and its level of severity76. And this could perhaps open up new leads for research. Vibration analysis could monitor repetitive motion signals, to detect abnormal vibration patterns and levels, which could allow the assessment of the overall structural condition of the trunk. But then one would still be left wondering whether that approach would reliably assess e.g. the risk of delamination and crack propagation, or ovalization and kinking.Nevertheless, it is now clear that if we stay within the limits of the theories that are the basis of methods such as e.g. the tree-statics of3, t/R rules used by Ref.1,15 or the ill-fated pulling tests as reported by Ref.77, then our mind will possibly not be able to devise the path of evolution. More

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Conducting an ecosystem survey during a pandemicCancellation of the survey aboard its primary National Oceanic and Atmospheric Administration (NOAA) survey vessel was overcome through acquisition of a charter for a commercial fishing vessel, following all COVID-19 guidelines (Supplementary Figs. 1 and 2). Initial plans were for 15 days at sea, rather than the 45 typically conducted. This lower effort, along with adverse weather and vessel constraints, resulted in only 25% of the average number of mid-water trawls being collected in the long-term core survey area (Fig. 1 and Supplementary Fig. 1). Despite the data reduction, this effort was one of the only fisheries independent surveys to occur on the US West Coast after the first lockdown in March 2020, furthering the need to evaluate impacts of reduced sampling and provide a robust synthesis of survey results for fishery management. Here we provide updated indices for a selection of ecologically and commercially important species that are critical for assessing ecosystem status.The 2020 sampling was spatially biased towards inshore (shallow) stations (Fig. 1) and thus the previously used method for calculating abundance indices (averaging log-transformed catch-per-unit-effort (CPUE), across all sampled stations) was expected to result in biased indices, in particular for species with strong nearshore (e.g., market squid Dorytheuthis opalescens, anchovy) or offshore (YOY Pacific hake Merluccius productus, myctophids Myctophidae, octopus Octopoda, krill) habitat associations (Supplementary Fig. 3). We confirmed that this bias does indeed occur by recomputing indices for the past 30 years, but using only 1 trawl from each of the 15 stations that were sampled in 2020, and comparing these indices to those using all available trawls (Fig. 2 and Supplementary Fig. 4). In contrast, model-based indices computed from equivalently subsampled past data did not show systematic bias due to the incorporation of spatial covariates (Fig. 2). Thus, although the average log CPUEs were well correlated with model-based indices for well-sampled years (1990–2019), average log CPUEs were determined to be inappropriate for 2020 reporting, and the model-based results were used to develop indices for all taxa for years 1990–2020.Fig. 2: A model for uncertainty and unavoidable effort reduction.a SE of log index vs. number of hauls for a given year from the delta-GLM model. Each point is a year, with 2020 indicated in red. Lines are predicted relationship between SE and sample size for each year, color indicating the mean log index for that year, scaled within taxa. b Relative bias in the index point estimate using 15 hauls from the 2020 stations vs. all hauls from all stations sampled in a given year, computed as (x2020 − xall)/xall. Boxplots show spread of results across all years, 1990–2019 (n = 30 independent years, center: median, box: first and third quartiles, whiskers: smallest and largest values no further than 1.5× IQR from the first and third quartiles; IQR, interquartile range). In the left panel, the index was computed by averaging values of log(CPUE + 1) from all available hauls in a given year. In the right panel, the index was computed from the maximum likelihood estimate (MLE) of a delta-GLM model with spatial covariates, as log(MLE + 1). For the model-based index, the x2020 estimate excludes hauls from the focal year but includes complete data from all other years. CPUE, catch-per-unit-effort; GLM, generalized linear model.Full size imageThe 2020 model-based indices for total rockfish and sanddab (Citharichthys spp.) were the second lowest on record and continued a decline from record high abundance levels observed during the 2014–2016 marine heatwave (Fig. 1)22,23. Pacific hake, myctophids, and octopus were also below average. In contrast, the 2020 index for adult northern anchovy continued a multi-year period of persistently high abundance (Fig. 1). Market squid indices were below average, following a mostly positive trend over the past 7 years. Following the steep decline in 2019, the krill index in 2020 was lower than average (Fig. 1); however, as discussed below, uncertainty may be underestimated for this highly patchy taxonomic group. As a consequence of the low sample sizes, a more rigorous evaluation of the trade-off between sample size (trawls) and uncertainty was conducted, as well as further evaluation of trends through application of existing ecosystem science tools.Quantifying uncertainty by resampling the pastFor most taxa, the uncertainty associated with the 2020 relative abundance estimate was the greatest in the time series, an intuitive result of the sparse sampling for that year (Figs. 1b and 2). The SE was estimated to be over three times the long-term average SE for rockfish and Pacific hake, myctophids, and octopus, and the largest (but less than double the long-term mean) for sanddabs and krill (Fig. 2a). By contrast, the uncertainty associated with the adult anchovy index was lower than the long-term average, due to the great abundance and high frequency of occurrence of anchovy in 2020, compared to years in past decades. This reflects the general trend of uncertainty (on the log scale) being greater for a given taxon when abundance is lower, which generally held for all taxa except krill in our explorations (Fig. 2 and Supplementary Fig. 5). Through time, the relative bias of the subset of stations (2020) vs. the full sample size is also consistently lower for the model-based solution compared to using the average estimate (Fig. 2 and Supplementary Fig. 6). There is also a strong relationship between the number of trawls conducted and the resulting error for each point estimate, with the error essentially doubling when the number of trawls is reduced from the long-term average of 62 to the 15 that were conducted in 2020 (Fig. 2a). By contrast, reducing the total number of trawls from 62 to 40 increases the relative error by just under 25%, while increasing the number of trawls from 62 to 90 only decreases the relative error by 16%. The extent to which the mean relative abundance scales that error up or down, regardless of sample size, is taxon specific. There is an approximate doubling of the error at lowest abundance levels relative to the highest levels for rockfish, sanddabs, hake, and market squid, an increase of more than fourfold over the same range for anchovies and octopus, and relatively modest scaling of the error for myctophids and krill (Fig. 2). This trade-off between survey effort and the error of the ecosystem indices provides critical guidance for future survey planning with respect to the complex trade-off between effort and uncertainty in the face of highly variable interannual catch rates.A seabird’s perspectiveThe Farallon Islands (National Wildlife Refuge) are located in the center of the survey region and host the largest breeding colony of common murre (Uria aalge) in the region (Fig. 1). Interannual variability of Farallon Island seabird population dynamics, reproduction, and foraging ecology are well understood and also track RREAS observations6,17. In particular, patterns such as alternating cycles of forage species occurrence and subsequent reproductive output are known to be linked to regional ocean and climate conditions17,20. Long-term observations of seabird diets in the Farallon Islands were fortunately not impacted by the pandemic. As common murre feed their chicks predominantly either juvenile rockfish or northern anchovy (Supplementary Fig. 7), and common murre prey selection is known to covary with prey abundance in the surrounding ecosystem17,20, these observations provide a critical data stream for evaluating 2020 rockfish and anchovy abundance index estimates from the limited trawl sampling. We updated regression models relating the proportion of rockfish and anchovy in murre diets, respectively, to model-based abundance indices for rockfish and anchovy using past data (Fig. 3). Linear models provided the best fit for YOY rockfish and anchovy, (r2 = 0.70; r2 = 0.58, respectively, both p  More

Alpha-pinene, the most abundant volatile found in all propolis, was also the most abundant volatile found in propolis produced in the Adriatic Sea coast of Italy, and its likely botanical origin was suggested to be native conifer trees from that region13. α-Pinene together with β-pinene were previously found as the two most abundant volatiles in propolis produced in the Rio Grande do Sul14 and Paraná3 states, in a not specified Brazilian propolis sample15, and propolis from South Africa16 and Uruguay1. It is noteworthy that South Africa and Uruguay are in similar latitudes to southern Brazil, indicating a characteristic profile related to that propolis location.Furfural, which was found in almost all samples, is a product of sugars dehydration commonly found in agricultural byproducts and was identified by SHS-GCxGC-TOF-MS in South African propolis, in which volatiles was extracted by heating at 45 °C/5 min, at concentrations ranging from trace to 11.3%16.β-Eudesmol was found as the most abundant volatile in propolis produced in France, Hungary, Bulgaria, and Northern Italy and was also the most abundant volatile in the distilled essential oil of Populus nigra buds, which likely is its primary botanical origin1. However, in our study, β-eudesmol was also found in the resins of AA as a minor volatile (from 0.7 to 2.4%). Additionally, AA resins were the only ones containing sabinene, α-thujene and α-bisabolol. Thereby, AA may be plant sources of these volatiles for brown propolis from southern Brazil. α-Bisabolol was also found as a major volatile in propolis produced in temperate zones of China and Turkish1.It is noteworthy that the temperature used to extract the volatiles, 180 °C, was higher than those commonly used for volatile profile characterization of 50–75 °C3,15,17. The degradation rate of pure monoterpenes at 120 °C varied greatly, depending on the compound, as it was 100% after 4 h for α-terpinene, 50% after 24 h for limonene, and 38% after 72 h for camphene18. The thermal degradation led to p-cymene, eucarvone and 1,2-epoxyde derivatives from limonene; thymol, ketoaldehydes, and eucalyptol from α-terpinene; and camphenilone, verbenone, and aromatic compounds from camphene18.Although verbenone was found in samples of our study (up to 1.3%), it was also tentatively identified in brown propolis extracted at 75 °C/30 min3, while p-cymene and verbenone were tentatively identified in Mediterranean propolis extracted at 60 °C/45 min17.Furthermore, McGraw et al.18 quoted Punsuvon, who reported the degradation at 90–130 °C (not specifying the time length) of pure α-pinene (23–37%), forming β-pinene, α-pinene oxide, α-campholenal, verbenol, pinocamphone, myrtenol and verbenone, and of pure β-pinene (22%), forming mainly myrtenol. From those thermal degradation products of α-pinene, some are reportedly relevant in propolis and conifer tissues, such as β-pinene, α-campholenal, myrtenol and verbenol1,15,19. Hence, the terpene diversity in natural products seems to result from naturally occurring chemical reactions catalyzed by microorganisms or enzyme systems20. At the same time, induced heating is a non-natural way to get it, and it is not simple to differentiate whether the terpene diversity is natural or induced by extraction conditions.The increase in temperature increased peak intensities up to 180 °C, and the number of peaks also increased, which likely indicates volatiles release from the propolis’s complex resinous/waxy matrix (Supplementary Fig. S1). However, the formation of low percentages of the tentatively identified carvone oxide (Supplementary Table S1), which likely had the added internal control l-carvone as a precursor, is an indication of oxidation. Nevertheless, l-carvone was pierced outside the samples within the vials. Thereby, it was more exposed to O2 and more prone to oxidation than the other volatiles present in the propolis/resins matrices.Concerning the multivariate analysis, the PCA showed that the SHS-GCMS method was sensible to discriminate propolis samples produced in different municipalities, even when the distance between the apiaries was 72 km (from ‘Beira do mato’ to ‘Vila Zulmira sede’). Moreover, the PCA indicates that A. angustifolia may be more attractive than Pinus species for bee foragers as a plant resin source to produce propolis.It is noteworthy that the possibility of Araucaria sp. resins be used as a botanical source for bees to produce brown propolis in southern Brazil was previously suggested by5, based on the identification of a single non-volatile compound, which is typical in some Araucaria species, in propolis samples from Paraná state. AA is a dominant species in subtropical and temperate rainforests in southern Brazil and adjacent areas. These areas were intensively explored over the nineteenth century. Nowadays, it is legally protected in permanent preservation areas since AA is endangered10. Therefore, the likely presence of AA resins in OP1 reinforces the need for sustainable preservation of natural environments since it may be related to OP1’s outstanding antioxidant activity4.From the tentatively identified volatiles in the hierarchical clustering heatmap, α-campholenal, α-phellandrene, β-bourbonene and trans-verbenol were found in essential oils of Pinus species19. This finding may indicate PT as another plant resin source for propolis production in those areas. To our knowledge, p-mentha-1,8-dien-7-ol was tentatively identified in plants from the Araucariceae family for the first time. p-Mentha-1,8-dien-7-ol, also known as perilla alcohol, is found in many plants’ essential oils, such as lavendin, peppermint, spearmint, and cherries21. Therefore, further studies should be conducted with authentic standards to confirm the identified volatiles in brown propolis and conifer resins from southern Brazil to be further used as phytochemical markers.In conclusion, there are indications that the resin from native Araucaria angustifolia is more attractive for bees to produce propolis in southern Brazil, although there is also an indication that non-native Pinus elliott and Pinus taeda are plant resin sources as well. However, the singularities on the chromatograms of propolis from each apiary/municipality illustrated in the heatmaps and the not complete overlap of the propolis and the conifer resins in the PCA may indicate that there are other botanical sources for bees to produce propolis within the permanent preservation areas of southern Brazil, which remain unknown. More

Coastline segmentsFor reasons of data availability and socioeconomic relevance, the analysis was limited to latitudes between 66° N and −60° S. In this area of interest, the world was divided in 1 arcmin (~2 km) grid cells. To define a logical position for the establishment of an efficient levee, the coastline location was derived from the OpenStreetMap68, moved 100 m land inward and smoothed. For every cell containing a coastline segment, coastline length and a coast-normal transect were derived at the center of segments resulting in 495.361 transects that are on average 1.1 km apart. Bootstrapping revealed that transect distances up to 2 km give very similar results. All transects stretch 4 km seaward and 4 km inland to fully capture most foreshores.Elevation dataA global intertidal bathymetry/elevation dataset from high-resolution EO data (USGS Landsat and Copernicus Sentinel-2), the Foreshore Assessment using Space Technology (FAST) intertidal elevation map69, was produced to compliment commonly used global data products with low resolution and higher inaccuracy in intertidal zones. Global coastlines were divided over 25000 tiles of each 40 × 40 km2. For these tiles, all available images were collected for the period between 1997 and 2017. Surface water was identified, using normalized difference spectral indices (NDSI, here SWIR1 and Green band) for all images (median of 317 images per tile) covering various tidal conditions, and the per pixel mean calculated to derive time-ensemble average (TEA) NDSI images. We developed a new technique to transform TEA images to intertidal elevation independently of in situ calibration data. TEA-NDSI images were normalized by the spatially averaged NDSI values of regions identified (using global elevation datasets) as land and water, respectively. This resulted in a single image per tile that represented the inundation probability for each pixel in the intertidal zone. The inundation probability represents the long-term average tidal inundation, because it was derived from a collection of images that span a time period similar to the tidal epoch (period of 19 years). Pixels having a probability of 1 represent permanent water, and have elevations less than or equal to the lowest astronomical tide (LAT), whereas land (p = 0) represents elevations higher than or equal to the highest astronomical tide (HAT). By deduction, p = 0.5 is equivalent to local mean sea level (LMSL). Tidal statistics from the global tide model FES2012 were used to couple the derived inundation probability to an elevation. The main source of bed level data originates from this map and has a 20 m horizontal resolution and typically a 30–50 cm vertical accuracy (RMSE = 0.52 m, MAE 0.42 m, as assessed at a number of sites with high quality elevation data (Supplementary Fig. 7)). Bathymetry data (GEBCO35; 30 arc-second horizontally, tens of metres vertically) and topography data (MERIT36; 3 arc-seconds, 2 m vertically) were merged to create a continuous bathymetry-elevation map by changing the vertical datum of MERIT from EGM96 to MSL by assuming 0 m +MSL at the OSM coastline. Global bathymetry datasets (e.g. GEBCO) and elevation datasets (e.g. SRTM and MERIT) lack accuracy (especially nearshore), but are commonly used17,18,23,34. The final bed level was constructed using FAST intertidal data where sufficient valid data points were available, complemented by the merged GEBCO-MERIT data where these points were lacking.Vegetation extentThe FAST coastal vegetation map69 was based on Landsat-8 and Sentinel-2 satellite images collected between 2013 and 2017. The map provides actual vegetation presence at 10 m resolution. Vegetation presence was obtained by applying an individual NDVI threshold per tile, with a total of 25,000 tiles, based on the yearly NDVI average and NDVI amplitude. The FAST coastal vegetation map is validated based on NDVI comparison with local measurements taken at Zuidgors, The Netherlands (R2 = 0.92) (Supplementary Fig. 8). If vegetation was present, the vegetation type was determined by global salt marsh32 and mangrove14 maps, complemented with Corine Land Cover30 (CLC, Europe only) and GlobCover v2.231 maps when there is no coverage. Determining global coastal vegetation extent is difficult and affected by eutrophication in coastal environments. This behaviour is observed on the coast along the Persian Gulf and the Red Sea. To improve accuracy only vegetated transects identified by the global salt marsh32 and mangrove14 map and confirmed by the FAST coastal vegetation map are included for these areas. Moreover, vegetated transects with a green belt width smaller than 250 m identified by GlobCover are excluded from the study for accuracy reasons (Supplementary Fig. 8). To avoid mixed vegetation types, the vegetation type was determined by the most dominant type. The vegetation width constituted of the sum of vegetated grid cells between the start and the end of the vegetated zone.Water level and wave dataThe design water levels were based on a combination of tide and storm surge for the selected probability of occurrence (return periods 2, 5, 10, 25, 50, 100 default, 250, 500, 1000 years) and came from the GTSR dataset34. SLR and subsidence were not taken into account because this study focuses on the present situation. Moreover, quantifying the future role of vegetated foreshores would not only require SLR scenarios but also an insight in the development of wetlands over time, which is strongly determined by local conditions such as sediment supply56,57,60. Offshore wave conditions were obtained from ERA-Interim33 re-analysis, based on data from 1979 till 2017 and reprojected to Dynamic Interactive Vulnerability Assessment (DIVA)70 points. Next, the Peak Over Threshold method was applied to construct representative values for the significant offshore wave height, Hs and the peak wave period Tp for all the return periods. The nearshore wave height was limited by the local water depth at the start of the (vegetated) foreshore using a breaker criterion (gamma = 0.55). This is a fairly low value considering the range of values cited in literature71 leading to conservative wave attenuation by vegetation results. Wave-bottom interactions in the sub-tidal zone and processes such as refraction and diffraction are not explicitly simulated. The conservative breaker criterion is chosen to implicitly account for these processes in a conservative manner. The wave period remained unchanged and the wave direction was assumed coast normal and wave growth along the transect due to wind effects was excluded. However, for the current study a more sophisticated approach to account for longshore wave variability based on topography was considered infeasible at the global scale and considered to yield limited outcome looking at the uncertainty in socioeconomic factors. The average Hs,offshore = 4.6 m (std = 2.0 m) and the average Hs,startforeshore = 0.7 m (std = 0.7 m).Profile constructionThe 8 kilometre coast-normal transects consisted of 321 gridpoints, thus a horizontal grid resolution of 25 m. We used four different methods: Foreshore method 1 (based on the FAST intertidal elevation map), Foreshore method 2–4 (based on MERIT-GEBCO). The properties of the FAST intertidal elevation map, MERIT and GEBCO are described under the header ‘Elevation data’. Foreshore method 1 produced the most accurate profiles and foreshore method 4 the least accurate profiles. The profile construction steps are described hereafter. Validity checks were performed to identify false indications of intertidal area in the FAST intertidal elevation map. Individual data points were marked invalid and removed in case: (1) MERIT points were situated above the surge level with a return period of 2 years, while data from the intertidal map indicated a lower elevation. (2) Data from the FAST intertidal map was situated at open sea. (3) Data from the FAST intertidal map along the transect dropped below a minimum range threshold of 10 cm. A fourth check was performed based on the continuity of the data. Data from the FAST intertidal map contain discontinuities along the profile. These continuities exist on pixel level due to the use of the modified normalized difference water index and in some instances cloud coverage was preventing full coverage. Lastly, discontinuities arise due to the presence of (high elevated) tidal flats and banks in coastal areas. (4) Data length was defined as the length of continuous data points along the transect. If the data length of a patch decreased below a threshold of 100 m, the points were marked invalid. Gaps between valid data patches were filled using linear interpolation if the gap was smaller than 250 m. Eventually, one, none or multiple valid data patches were found along the transects. See Supplementary Fig. 2 for example transects.Global coastline shapes range from straight sandy coastal stretches to complex coastlines often found in estuaries. With a transect length of 8 km, the start and the end of the transects could both be situated on land, hampering an unambiguous identification of the foreshore of interest. We designed the algorithm such that the last foreshore was selected. For profiles using data from the FAST intertidal map (foreshore method 1, 50.9% of populated susceptible coastlines), the last valid patch corresponds to the last foreshore. The inclusion of tidal flats as part of the foreshore was determined based on the gap length. In case no (sufficient, thus not satisfying the minimum data length criterion of 100 m) valid data was available from the FAST intertidal map based on the four described checks, the profile was based on a merged GEBCO-MERIT set (methods 2, 3 and 4), respectively, 46.1%, 3.0% and 0.01%. For the second method, data points were selected between a minimum threshold of −2 m MSL and a maximum threshold equal to the surge level with a return period of 2 years. Next, for the selected points the direction of the slope was determined by comparing elevation between the data point concerned and the next data point. This resulted in patches of upward sloping sets of data points between the minimum and maximum threshold. Similar to foreshore method 1, the validity of the patches was checked using data length, gap length and the corresponding thresholds of 100 m and 250 m. The start and the end of the foreshore were determined by the first and last valid point of the last patch. Foreshore method 3 was used if not sufficient foreshore data were available to satisfy the minimum data length threshold (100 m). In these cases, the start of the foreshore was defined as the first upcrossing intersection with −2 m MSL along the transect. The end of the foreshore corresponded to the intersection between the elevation profile and the governing surge level with a return period of 2 years. Foreshore method 4 was used if no start and or end of the foreshore could be found. In this case the start and/or end point of the foreshore corresponded to the first and last data point, respectively.In some cases, elevation for the end of the foreshore was missing due to several reasons. First, the upper part of the intertidal zone was sometimes missing from the FAST intertidal map, due to low frequency of inundation of the upper intertidal zone or cloud cover. Second, bed elevation in mangrove belts was hard to define based on satellite imagery, as the canopy is detected as the earth surface. These uncertainties were counteracted by consulting the mangrove and salt marsh maps. If vegetation was present in one of these maps, the derived foreshore was extended until the end of the vegetated zone. An elevation equal to the surge level with a return period of 2 years was chosen as elevation for extended foreshore points with an elevation exceeding this surge level.Vegetation parametersAs deducting the type and size of mangrove trees and salt marshes from EO data at global scale is not possible (yet), the current modelling approach relies on field and literature observations. For the scope of this research the properties of the mangrove trees occurring at the seaward side of the mangrove belt are the most relevant. To avoid overestimation of wave attenuation in young mangrove forests, the mangrove dimensions are chosen such to be representative for young fringing pioneering mangroves up to a height of 3 m that are practically vertically uniform compared to mature trees. The modelling approach uses four parameters to represent vegetation: height, diameter, number of stems and drag coefficient. The exact characteristics are based on observations in literature8,9,72,73,74,75,76 (N = 30 m−2, d = 35 mm, h = 3.0 m).High quality observations on wave attenuation by mangroves under storm conditions do not exist. For the drag coefficient the theoretical value, 1, of a rigid cylinder is chosen, because mangrove trunks can be considered rigid. For salt marshes a winter state representative as found in NW Europe is chosen. The values are defined based on FAST field tests (Romania, UK, Spain and the Netherlands) and literature10,24,77,78 (N = 1225 m−2, d = 1.25 mm, h = 0.30 m). A drag coefficient (CD) of 0.19 is chosen, which is the lower limit found during large-scale flume tests10. The drag coefficient depends on biophysical characters as well hydrodynamics. The drag coefficient represents drag due to skin friction and pressure differences, but also effects like swaying motion of stems24. The 1D modelling approach takes into account gaps in vegetation cover, e.g. due to the presence of channels. Zonation of vegetation types is not implemented, because this level of detail is insignificant in relation to the inaccuracies induced by the use of global datasets.Wave attenuation modelTo determine wave attenuation along the foreshore transects and the resulting significant wave heights relevant for the flood defence on a transect, we used a lookup-table approach. The lookup table was generated by combining 668,304 model output values for different combinations of foreshore slopes, vegetation covers and hydrodynamic conditions. The table contained wave heights modelled by XBeach79 in surfbeat mode (a nearshore numerical wave model that accounts for the presence of vegetation) at regular intervals along a steady slope, both with and without vegetation. XBeach uses for wave-vegetation interaction the rigid cylinder80 approach and includes an energy sink term to the wave energy balance to implement wave dampening81. We used conservative vegetation characteristics, winter state salt marshes and young pioneering mangroves. We characterized foreshores by their width and slope. The foreshore profile was the same for simulations with and without vegetation. The foreshore width was determined by calculating the distance between the start and the end of the foreshore. The slope was estimated using a linear regression. This approach has two advantages over detailed modelling of wave attenuation over all transects: it is much quicker, allowing for iterative improvements of the workflow and it does not suggest the precision one would expect from detailed models but cannot be delivered with global data. Average Hs,endforeshore,noveg = 0.6 m (std = 0.5 m) and Hs, endforeshore,veg = 0.3 m (std = 0.4 m).Coastline susceptible to flooding, urban and rural extents and population densityTo assess the need for coastal flood defences, we made a distinction between areas susceptible to coastal flooding and higher, non-susceptible areas. We determined susceptible areas based on possible inundation using coastal flood maps of 1 km resolution for a 1/1000 year surge level. These maps were created with a global geographic information system (GIS) based inundation model that is forced with a spatially varying sea level, accounting for attenuation of the water level due to land surface roughness82. A method that is more sophisticated compared to a simple ‘bathtub’ inundation method. Topographic features, as visible in MERIT, protecting the land from flooding are considered. To classify coastlines as urban or rural a distinction was made based on gridded population from the LandScan database83 using the 2UP model84. A transect is characterized ‘urban’ if it intersects at least one cell with an urban population with a minimum of 1. Populated coasts have been identified by assigning the population density of the population susceptible to flooding in the proximity of the transects. We used WorldPop201785 population data and assigned population to the transects using a buffer of 15 kilometre radius. The population density is the division of the assigned population and the total area of the assigned cells. This procedure is repeated for buffer radius of 5, 10 and 20 km, giving fairly comparable outcomes. Following this approach we found a ratio between rural and urban transects of 73/27.Levee crest heightsThe empirical EuroTop formulations47 gave the required levee heights with respect to water levels and wave heights, assuming the presence of a levee at the end of the vegetated foreshore. We hereby neglected the position and characteristics of levees present in the current situation, as no global dataset of coastal protection structures exists. The assumed levee had a standard 1:3 levee profile without berms and an allowed overtopping discharge of 1 l s−1 m−1. These parameters are representative for simple, low-cost levees in developing countries but conservative for well-constructed and maintained levees. Consequently, savings on levee heights in countries with strict protection standards are overestimated, as reduction in required levee height due to vegetation presence is likely less than predicted here. However, this may be balanced out by the fact that we calculated with an average national construction cost per kilometre and levees applying to stricter protection standards may actually be more expensive (Supplementary Fig. 5).Costs for levee construction and crest height reductionThe calculated levee crest height reductions were monetized using a levee unit price per kilometre length per metre heightening. We used an unit investment costs of levees (metre heightening per kilometre length) of USD 7.0 million42. This estimate represents an average of construction costs in the USA and the Netherlands stated in several studies86,87,88,89. It pertains to all investments costs, including ground work, construction, engineering costs, property or land acquisition, environmental compensation, and project management. Investment costs per metre heightening are well described by a linear function without intercept90. They concluded that for large-scale studies it is sufficient to assume linear costs for each metre of heightening, including the initial costs and the 95% confidence range is between 3x and x/3, where x is the unit cost value. Subsequently we applied three unit levee investment cost prices (low: USD 2.33 million, mid: USD 7.0 million, high: USD 21 million) in line with previous studies42,90. These cost estimates were then adjusted for all other countries by applying construction index multipliers (based on civil engineering construction costs91), to account for differences in construction costs across countries92. Costs were converted to USD2005 power purchasing parity (PPP), to be consistent with the SSPs, using GDP deflators from the World Bank (https://data.worldbank.org/), and annual average market exchange rates between Euros and USD taken from the European Central Bank (unit levee cost per country = unit levee cost x construction index per country / PPP MER rate 2005 index per country). Example: mid unit levee costsUSA = 7.0 ×1 / 1 = 7.0 million USD2005 PPP km m−1. If for a country data was not available in the database, we used the average of all countries in the same World Bank income group. For the reference year 2005, this applies to Western Sahara (ESH), North-Korea (PKR) and Somalia (SOM).ReliabilityA scoring table was used to get insight in the reliability of the results of the global analysis. Results were grouped into four reliability classes ranging from “poor” to “very good”. Transects were placed in these classes based on data accuracy for three characteristics: hydrodynamics, vegetation and profile elevation. In Supplementary Fig. 6 the (sub) results of the analysis are presented. The first category, hydrodynamics, included known inaccuracies in the hydrodynamic data (GTSM and ERA-I). Data from the GTSM model was considered less reliable in areas with a low tidal range and/or with tropical storms, such as cyclones or hurricanes, as those were not included in our analyses. Also wave data from ERA-I are less reliable in these areas, because the effects of tropical storms are flattened due to the relatively coarse grid size. Hence, transects in these areas were pinpointed by linking them to NOAA data of historical hurricane tracks93. In Supplementary Fig. 6B, areas where tropical storms occur can clearly be recognized. In addition, the Mediterranean Sea, the Red Sea, the Black sea and the Caspian sea stand out in inaccuracy, because of limited tidal action.Reliability of vegetation characteristics was determined by data source and vegetation width. For transects with extensive vegetation widths, crest height reduction was less sensitive for possible deviations of the vegetation width, due the non-linear relation between vegetation width and wave reduction. Vegetation cover proved most reliable in areas where data from the salt marsh32—and mangrove map14 were available. Hence, this resulted in a ‘good’ score (Supplementary Fig. 6C). Only in cases of extensive vegetation presence was a ‘very good’ score assigned. Transects were appointed as “very good” if vegetation extended 500 m for mangroves, and 1000 m for salt marshes. These thresholds are chosen based on our model results, which show that after ~500 m (salt marshes) and 1000 m (mangroves) maximum reduced wave transmission by foreshore vegetation is reached. Vegetation cover reliability in Europe was classified as ‘good’, due to reliable vegetation type classification based on CLC30 and the salt marsh map32 in combination accompanied by relatively small vegetation widths. The reliability of the derived vegetation characteristics is especially lacking at the east coast of Canada, at Latin America’s south coast, at Africa’s coasts facing the Mediterranean Sea, coasts along the Red Sea and the Persian Gulf, and along the coasts of China, Japan and Russia. For example, in the Persian Gulf states the vegetation presence map tends to falsely identify foreshores as vegetated.The time-ensemble average (TEA) technique applied for the FAST intertidal elevation map relies on the availability of a reasonable number of images at different tidal stages where the differences in horizontal extent of water coverage can be identified, thus allowing a composite of inundation frequency to be derived. However, the technique is limited by the effective sensor resolution (~30 m, including uncertainty in georeferencing) relative to the horizontal extent of changes in inundation, a function of the tidal range and bed slope. Hence, changes in tidal water extent in microtidal or very high bed-slope regions tend to be too small for reliable discerning differences, leading to poor performance of the technique. However, the merged GEBCO-MERIT dataset was considered less reliable than the FAST intertidal map, based on the resolution and the merging of the two underlying datasets in the intertidal zone. In addition, MERIT tends to overestimate the elevation in mangrove areas, as it measures the canopies as the earth’s surface. Besides the elevation data, the foreshore definition method is used as a profile reliability indicator. The total score per transect is given by the sum of the sub-scores. The sub-scores are normalized to give equal weight to the scoring categories.ValidationFor validation of our method to assess vegetation presence, a comparison of 280 randomly located transects with aerial imagery was carried out. The area accessed in the global assessment was divided in tiles of 90 degrees longitude and 15 degrees latitude. From each tile 6 vegetated and 2 non-vegetated transects were selected. Next, a reference dataset was created by manually identifying vegetation presence using present imagery. Lastly, the vegetation width derived by the model and the manually derived set were compared (Supplementary Fig. 8). For this comparison we made three distinctions, based on (1) vegetation type, (2) foreshore derivation method and (3) vegetation cover source. Comparison showed that the used algorithm on global EO data performs satisfactorily (Supplementary Fig. 8), but in some cases tends to assign a vegetation cover of up to 250 m where there is none. Deviation between observation and the global assessment, is caused by methodological error in the global assessment and inaccuracy in the global datasets, e.g. different timestamps are inevitably compared. This would induce an exaggeration of the effect of vegetation. However, due to the limited dimension of the vegetation extent, the threshold for substantial crest height reduction is falsely exceeded in not more than 2.4% of the cases and the effect is largely balanced out by underestimation of the vegetation cover at larger lengths.To validate wave reduction by vegetation calculated through our lookup table approach, we compared results with local modelling results for the South-Western part of the Netherlands for 38 vegetated transects. The numerical model SWAN94 in stationary mode was used to translate wave conditions from offshore to nearshore. The simulations were performed with a grid size of 0.01 deg and bathymetry from EMODNET95. Extreme water levels were included by a water depth correction, using data from GTSR18. Both wind and wave boundary conditions were derived from the earlier described ERA-I re-analysis. The governing wave direction was based on the average of the fifteenth highest wave events in the available wave data. The wind direction was assumed to be aligned with the wave direction. A parametric JONSWAP spectrum shape was used, using a peak enhancement factor of 3.3 and directional spreading of 20 degrees. Foreshore profiles were constructed using an approach similar to foreshore method 2 in the global study but using local high-resolution bathymetry and topography data. Vegetation width was extracted from the salt marsh map32, which was confirmed locally using aerial imagery. Foreshore wave propagation was determined using XBeach in surfbeat mode79.Our results showed an overestimation of the water depth at the start of the vegetated zone by 0.73 m on average. In addition, the global model derived milder slopes in comparison to the local analysis for narrow vegetated transects. The largest errors were found further away from the mouth of the estuary. Here, the deviation between the wave calculated by SWAN and the depth limited approach is largest. The wave height at the start of the vegetated zone was overestimated on average by 1.12 m, due to the complex geometry and the sheltered configuration of the estuary. The algorithm approximated the wave transmission reduction (RMSE 13%) and the levee crest height reduction relative to the required crest height without vegetation presence (RMSE 19%) with reasonable accuracy (Supplementary Fig. 9).Sensitivity analysisA sensitivity analysis has been performed to provide insight in the uncertainty in the presented potential global levee costs savings. The analysis focused specifically on single key parameters, such as the levee unit cost, the critical overtopping discharge and the wave breaker index. High, mid and low levee unit cost scenarios are taken from previous studies42,90. A high, mid, low for the critical overtopping discharge are respectively 10, 1 and 0.1 l s−1 m−1 to incorporate the quality of the levee cover47. We chose RP10 and RP1000 for, respectively, the low and high storm return period scenario. The uncertainty spread of vegetation width is based on the 75% confidence intervals of the underestimated and overestimated vegetation widths of mangroves (+436 m, −136 m) and salt marshes (+597 m, −104 m) in the vegetation presence validation study. For the breaker index we solely chose a high scenario of 0.78, because the index of the global assessment (0.55) was already quite conservative71. For topography we applied a range corresponding to the typical vertical accuracy of the FAST intertidal elevation dataset (±50 cm). Two representative subsets of 500 transects for respectively mangroves and salt marshes have been derived using the clustering method k-means96, based on hydrodynamic conditions, vegetation cover, profile characteristics and geographical location. With these subsets, we repeated the analysis procedure of the global assessment for the sensitivity scenarios. The results point out that the largest spread is caused by the uncertainty in the unit levee cost with −66% and +200% for, respectively, the low and high scenario with respect to the global reference analysis. The other scenarios: topography (−39%, +47%), critical overtopping discharge (−40%, + 40%), storm return period (−28%, +34%), vegetation width (−28%, +39%), breaker index (+21%) (Supplementary Fig. 10). Larger water depths result in a decrease of depth-induced wave energy dissipation and more dissipation due to wave-vegetation interaction, which explains the outcomes of the topography sensitivity results. Similarly, an increase of the storm return period or the breaker index shifts the ratio of wave energy dissipation by wave-bottom interaction and wave-vegetation interaction. The coastal protection costs by vegetation are sensitive to critical overtopping discharge changes, because of the non-linear relation between the wave height in front of the levee and the overtopping discharge47. More

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