Ecology

Chaigneau, A., Gizolme, A. & Grados, C. Mesoscale eddies off Peru in altimeter records: identification algorithms and eddy spatio-temporal patterns. Prog. Oceanogr. 79, 106–119 (2008).ADS 
Article 

Google Scholar 
McGillicuddy, D. J. Jr et al. Influence of mesoscale eddies on new production in the Sargasso Sea. Nature 394, 263–266 (1998).ADS 
CAS 
Article 

Google Scholar 
Dufois, F. et al. Anticyclonic eddies are more productive than cyclonic eddies in subtropical gyres because of winter mixing. Sci. Adv. 2, 1–7 (2016).Article 

Google Scholar 
Godø, O. R. et al. Mesoscale eddies are oases for higher trophic marine life. PLoS ONE 7, e30161 (2012).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Chelton, D. B., Gaube, P., Schlax, M. G., Early, J. J. & Samelson, R. M. The influence of nonlinear mesoscale eddies on near-surface oceanic chlorophyll. Science 334, 328–333 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Sarmiento, J. L. et al. Response of ocean ecosystems to climate warming. Global Biogeochem. Cycles 18, GB3003 (2004).ADS 
Article 
CAS 

Google Scholar 
Bell, J. D. et al. Diversifying the use of tuna to improve food security and public health in Pacific Island countries and territories. Mar. Policy 51, 584–591 (2015).Article 

Google Scholar 
Della Penna, A. & Gaube, P. Mesoscale eddies structure mesopelagic communities. Front. Mar. Sci. 7, 454 (2020).ADS 
Article 

Google Scholar 
Braun, C. D. et al. The functional and ecological significance of deep diving by large marine predators. Ann. Rev. Mar. Sci. 14, 129–159 (2022).PubMed 
Article 

Google Scholar 
McGillicuddy, D. J. Jr Mechanisms of physical-biological-biogeochemical interaction at the oceanic mesoscale. Ann. Rev. Mar. Sci. 8, 125–159 (2016).PubMed 
Article 

Google Scholar 
Fennell, S. & Rose, G. Oceanographic influences on deep scattering layers across the North Atlantic. Deep-Sea Res. Part I Oceanogr. Res. Pap. 105, 132–141 (2015).ADS 
Article 

Google Scholar 
Duffy, L. M. et al. Global trophic ecology of yellowfin, bigeye, and albacore tunas: understanding predation on micronekton communities at ocean-basin scales. Deep-Sea Res. Part II Topical Stud. Oceanogr. 140, 55–73 (2017).ADS 
Article 

Google Scholar 
Gaube, P. et al. Mesoscale eddies influence the movements of mature female white sharks in the Gulf Stream and Sargasso Sea. Sci. Rep. 8, 7363 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Braun, C. D., Gaube, P., Sinclair-Taylor, T. H., Skomal, G. B. & Thorrold, S. R. Mesoscale eddies release pelagic sharks from thermal constraints to foraging in the ocean twilight zone. Proc. Natl Acad. Sci. USA 116, 17187–17192 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Doyle, T. K. et al. Leatherback turtles satellite-tagged in European waters. Endanger. Species Res. 4, 23–31 (2008).Article 

Google Scholar 
Pauly, D. & Christensen, V. Primary production required to sustain global fisheries. Nature 374, 255–257 (1995).ADS 
CAS 
Article 

Google Scholar 
Lynham, J., Nikolaev, A., Raynor, J., Vilela, T. & Villaseñor-Derbez, J. C. Impact of two of the world’s largest protected areas on longline fishery catch rates. Nat. Commun. 11, 979 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Polovina, J. J., Abecassis, M., Howell, E. A. & Woodworth, P. Increases in the relative abundance of mid-trophic level fishes concurrent with declines in apex predators in the subtropical North Pacific, 1996-2006. Fish. Bull. 107, 523–531 (2009).
Google Scholar 
Royer, T. C. Ocean eddies generated by seamounts in the North Pacific. Science 199, 1063–1064 (1978).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Liu, Y. et al. Eddy analysis in the subtropical zonal band of the North Pacific Ocean. Deep-Sea Res. Part I Oceanogr. Res. Pap. 68, 54–67 (2012).ADS 
Article 

Google Scholar 
Bernstein, R. L. & White, W. B. Time and length scales of baroclinic eddies in the central North Pacific Ocean. J. Phys. Oceanogr. 4, 613–624 (1974).ADS 
Article 

Google Scholar 
Maunder, M. N. & Punt, A. E. Standardizing catch and effort data: a review of recent approaches. Fish. Res. 70, 141–159 (2004).Article 

Google Scholar 
Woodworth, P. A. et al. Eddies as offshore foraging grounds for melon-headed whales (Peponocephala electra). Mar. Mammal Sci. 28, 638–647 (2012).Article 

Google Scholar 
Gaube, P. et al. The use of mesoscale eddies by juvenile loggerhead sea turtles (Caretta caretta) in the southwestern Atlantic. PLoS ONE 12, e0172839 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Chambault, P. et al. Swirling in the ocean: immature loggerhead turtles seasonally target old anticyclonic eddies at the fringe of the North Atlantic Gyre. Prog. Oceanogr. 175, 345–358 (2019).ADS 
Article 

Google Scholar 
Gaube, P., McGillicuddy Jr, D., Chelton, D., Behrenfeld, M. & Strutton, P. Regional variations in the influence of mesoscale eddies on near-surface chlorophyll. J. Geophys. Res. Oceans 119, 8195–8220 (2014).Waga, H., Kirawake, T. & Ueno, H. Impacts of mesoscale eddies on phytoplankton size structure. Geophys. Res. Lett. 46, 13191–13198 (2019).ADS 
Article 

Google Scholar 
Irigoien, X. et al. Large mesopelagic fishes biomass and trophic efficiency in the open ocean. Nat. Commun. 5, 3271 (2014).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Chen, Y.-lL. et al. Biologically active warm-core anticyclonic eddies in the marginal seas of the western Pacific Ocean. Deep Sea Res. Part I 106, 68–84 (2015).CAS 
Article 

Google Scholar 
Harke, M. J. et al. Microbial community transcriptional patterns vary in response to mesoscale forcing in the North Pacific Subtropical Gyre. Environ. Microbiol. 23, 4807–4822 (2021).CAS 
PubMed 
Article 

Google Scholar 
Hawco, N. J. et al. Iron depletion in the deep chlorophyll maximum: mesoscale eddies as natural iron fertilization experiments. Global Biogeochem. Cycles 35, e2021GB007112 (2021).ADS 
CAS 
Article 

Google Scholar 
Klevjer, T. A. et al. Large scale patterns in vertical distribution and behaviour of mesopelagic scattering layers. Sci. Rep. 6, 19873 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Behrenfeld, M. J. et al. Global satellite-observed daily vertical migrations of ocean animals. Nature 576, 257–261 (2019).CAS 
PubMed 
Article 

Google Scholar 
Madigan, D. J. et al. Water column structure defines vertical habitat of twelve pelagic predators in the South Atlantic. ICES J. Mar. Sci. 78, 867–883 (2021).Article 

Google Scholar 
Arostegui, M., Gaube, P. & Braun, C. Movement ecology and stenothermy of satellite-tagged shortbill spearfish (Tetrapturus angustirostris). Fish. Res. 215, 21–26 (2019).Article 

Google Scholar 
Lehodey, P., Senina, I. & Murtugudde, R. A spatial ecosystem and populations dynamics model (SEAPODYM)—modeling of tuna and tuna-like populations. Prog. Oceanogr. 78, 304–318 (2008).ADS 
Article 

Google Scholar 
Varghese, S. P., Somvanshi, V. S. & Dalvi, R. S. Diet composition, feeding niche partitioning and trophic organisation of large pelagic predatory fishes in the eastern Arabian Sea. Hydrobiologia 736, 99–114 (2014).CAS 
Article 

Google Scholar 
Ward, P. & Myers, R. A. Inferring the depth distribution of catchability for pelagic fishes and correcting for variations in the depth of longline fishing gear. Can. J. Fish. Aquat.Sci. 62, 1130–1142 (2005).Article 

Google Scholar 
Kai, E. T. et al. Top marine predators track Lagrangian coherent structures. Proc. Natl Acad. Sci. USA 106, 8245–8250 (2009).ADS 
CAS 
Article 

Google Scholar 
Lima, I. D., Olson, D. B. & Doney, S. C. Biological response to frontal dynamics and mesoscale variability in oligotrophic environments: biological production and community structure. J. Geophys. Res. Oceans 107, 25-1–25-21 (2002).Article 

Google Scholar 
Spall, S. A. & Richards, K. J. A numerical model of mesoscale frontal instabilities and plankton dynamics—I. model formulation and initial experiments. Deep-Sea Res. Part I Oceanogr. Res. Pap. 47, 1261–1301 (2000).ADS 
Article 

Google Scholar 
Siegelman, L., O’Toole, M., Flexas, M., Rivière, P. & Klein, P. Submesoscale ocean fronts act as biological hotspot for southern elephant seal. Sci. Rep. 9, 5588 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Lévy, M., Ferrari, R., Franks, P. J., Martin, A. P. & Rivière, P. Bringing physics to life at the submesoscale. Geophys. Res. Lett. https://doi.org/10.1029/2012GL052756 (2012).Article 

Google Scholar 
Guidi, L. et al. Does eddy-eddy interaction control surface phytoplankton distribution and carbon export in the North Pacific Subtropical Gyre? J. Geophys. Res. Biogeosciences https://doi.org/10.1029/2012JG001984 (2012).Article 

Google Scholar 
Chow, C. H., Cheah, W., Tai, J. H. & Liu, S. F. Anomalous wind triggered the largest phytoplankton bloom in the oligotrophic North Pacific Subtropical Gyre. Sci. Rep. 9, 15550 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Guo, M., Xiu, P., Chai, F. & Xue, H. Mesoscale and submesoscale contributions to high sea surface chlorophyll in subtropical gyres. Geophys. Res. Lett. 46, 13217–13226 (2019).ADS 
Article 

Google Scholar 
Klein, P. et al. Ocean-scale interactions from space. Earth Space Sci. 6, 795–817 (2019).ADS 
Article 

Google Scholar 
Martin, A. et al. The oceans’ twilight zone must be studied now, before it is too late. Nature 580, 26–28 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
St. John, M. A. et al. A dark hole in our understanding of marine ecosystems and their services: perspectives from the mesopelagic community. Front. Marine Sci. 3, 31 (2016).
Google Scholar 
Bigelow, K., Musyl, M. K., Poisson, F. & Kleiber, P. Pelagic longline gear depth and shoaling. Fish. Res. 77, 173–183 (2006).Article 

Google Scholar 
Brodziak, J. & Walsh, W. A. Model selection and multimodel inference for standardizing catch rates of bycatch species: a case study of oceanic whitetip shark in the Hawaii-based longline fishery. Can. J. Fish. Aquat.Sci. 70, 1723–1740 (2013).Article 

Google Scholar 
Woodworth-Jefcoats, P. A., Polovina, J. & Drazen, J. Synergy among oceanographic variability, fishery expansion, and longline catch composition in the central North Pacific Ocean. Fish. Bull. 116, 228–239 (2018).Article 

Google Scholar 
Boggs, C. H. Depth, capture time, and hooked longevity of longline-caught pelagic fish: timing bites of fish with chips. Fish. Bull. 90, 642–658 (1992).
Google Scholar 
Walsh, W. A. & Brodziak, J. Applications of Hawaii longline fishery observer and logbook data for stock assessment and fishery research. NOAA Tech. Memo. 57, 62 (2016).
Google Scholar 
Walsh, W. A. & Brodziak, J. Billfish CPUE standardization in the Hawaii longline fishery: model selection and multimodel inference. Fish. Res. 166, 151–162 (2015).Article 

Google Scholar 
Gilman, E., Chaloupka, M., Fitchett, M., Cantrell, D. L. & Merrifield, M. Ecological responses to blue water MPAs. PLoS ONE 15, e0235129 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Portner, E. J., Polovina, J. J. & Choy, C. A. Patterns in micronekton diversity across the North Pacific Subtropical Gyre observed from the diet of longnose lancetfish (Alepisaurus ferox). Deep-Sea Research Part I 125, 40–51 (2017).ADS 
Article 

Google Scholar 
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

Google Scholar 
Hartig, F. DHARMa: Residual diagnostics for hierarchical (multi-level/mixed) regression models. R package version 0.3.3.0 http://florianhartig.github.io/DHARMa/ (2020).Jackson, C. H. Multi-state models for panel data: the msm package for R. J. Stat. Softw. https://doi.org/10.18637/jss.v038.i08 (2011).Article 

Google Scholar 
Bates, D. et al. lme4: Linear mixed-effects models using ’Eigen’ and S4. R package version 1.1-25 https://github.com/lme4/lme4/ (2020).Lenth, R. et al. emmeans: Estimated marginal means, aka least-squares mean. R package version 1.7.2 https://github.com/rvlenth/emmeans (2022).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020); http://www.r-project.org/ More

In line with the results of present study, we suggest that the exploitation of forage resources by agro-pastoralists occurs in a non-random manner. The use of forage resources is guided by a series of functional characters related to palatability and nutritional value, which determine preferential use due to the better quality of resource. At the same time, we understand that forage uses are complex and multifactorial in nature, and regulated in a substantial way by seasonality and ecological factors (Fig. 5), such as the availability of plant resources and local diversity.Figure 5Diagrammatic representation for the effects of vegetation cover and seasonality on forage resource selection in Dry Forests. Image created with Microsoft Office 2019 PowerPoint (www.office.com).Full size imageThe differences of plant species cited between areas reveal the positive effect of vegetation cover on the use and knowledge of plants by agro-pastoralists. Our findings reveal that the greater number of plant species mentioned by agro-pastoralists in Area II is directly associated with greater availability of resources in this area, as long as we consider vegetation cover as availability of resources, which allows different species to be used throughout the year. On the other hand, in regions with low vegetation cover (Area I), the low availability of resources limits the use and knowledge of plants by residents, which can lead to greater pressure on a small set of available species. Such findings reinforce the importance of vegetation cover for ecosystem provision of goods and services to human populations that depend directly or indirectly on these services.The most represented families found in the present study have also been reported in several other ethnobotanical studies6,16,17,29, with emphasis on Fabaceae and Poaceae, which are recognized for their high forage potential, which derives, above all, from high palatability and nutritional value30. Simultaneously, citations mostly for native species reflect the importance and potential of Caatinga resources as important components of the ruminant diet11, both for the woody and herbaceous strata, corroborating the estimate in the literature that 70% of vegetation has potential use as forage31.The characteristic seasonality of vegetation, on the other hand, represents a limiting factor for forage productivity, culminating in high fluctuations in quality and availability, as well as changes in the dominance of different strata and composition of forage species throughout the seasons11,32. The seasonal distribution of species explains the similarity of seasons between areas, with a higher similarity percentage for the dry seasons, since there is less availability of resources to be exploited compared to the rainy season. In this context, the potentially used species are commonly accessible woody species in both areas. However, during the rainy season, the high availability of herbaceous plants regulates different uses (Fig. 4), but even so, they also exhibit relatively similar patterns, mainly due to the woody component that denotes the common demand by ruminants at the beginning of this season.The effect of climatic variables on vegetation use patterns was documented by16,17, both of which showed greater richness in the use of herbaceous forage during the rainy season, a finding that reflects the seasonal distribution—restriction to that season—and decrease in the qualitative character of annual species33. At the same time, it also reflects the greater number of unique species for the rainy season. However, when compared to woody strata, significant differences in terms of richness are not found because although the diversity of herbaceous species in the Caatinga is greater24, it is much less known than that of the tree-shrub stratum11.Agro-pastoralists even characterize animal preferences for herbaceous stratum, but as its diversity is immense and ephemeral, they claim to have limited ability to identify the species. The high abundance of resources in the rainy season also reduces the concern with forage use, which implies less attention to the species that are consumed. In contrast, woody species, due to multiple uses and greater availability over time, tend to be better known10,34, with a different effect in the dry season making the optimal foraging pattern in this period inherent to the knowledge of agro-pastoralists35.In addition, according to the ecological appearance hypothesis, there is a general tendency for less apparent species to be neglected by populations36. Some studies have corroborated the hypothesis within the context of forage use, with woody species being cited more and having more uses6,15. In addition, people tend to focus on resources whose supply is given continuously10, which may explain why woody species are well represented in both seasons.Security in the provisioning of ecosystem services is an essential component for local populations, and thus woody species are highly valued because they reflect predictability of use15,35. This can be a particularly influential criterion because perennial or late leaf deciduous species, such as Cynophalla flexuosa and Myracrodruon urundeva, had significant amounts of citations and perceptions employing high valuation, as represented by some statements by some interviewees: “É um refrigero na seca” (it is savage in the dry season), “É uma ração boa na seca” (it is a good food in the dry season).In turn, differences in richness of the species cited by the two areas corroborate our first hypothesis that populations inserted in environments with greater vegetation cover tend to cite more species. In line with these findings, considerable floristic dissimilarity was also found between the two areas, given the exclusivity of species. Such dissimilarity may suggest particularities in the vegetation attributes of each area, such as greater floristic diversity7,37,38.Since anthropic processes are irregularly distributed in space, variation in the provisioning of ecosystem services by vegetation also occurs, and influences different collection profiles39. On the other hand, areas with greater species richness have been shown to have greater use patterns6,7. The larger number of species cited as woody and native for Area II is, therefore, associated with greater general richness, as well as herbaceous species present in the rainy season. In contrast, common species are reflected in trends of similar foraging patterns, as well as the presence of common species between areas38. In addition to different levels of disturbance, differences in floristic composition between areas may also be due to edaphic variation40.Our second hypothesis was refuted because the difference in the richness of exotic species between the areas. Plausible explanations for this finding are that, in general, exotic herbaceous species are commonly used for forage in the semi-arid region of Brazil41. Herbaceous species comprise the primary component of the ruminant diet. However, in the midst of their occurrence restricted to the short rainy period, exotic species, mainly of Fabaceae and Poaceae, have been introduced to increase the forage availability, which currently represents an important attribute of forage resources in the Caatinga41,42,43. At the same time, and to also increase the availability of forage resources, the cultivation of species by agro-pastoralists may be common in their properties44, mainly exotics, such as Prosopis juliflora, that have high adaptive potential and governmental incentives45.Regarding use patterns, according to the data presented here it is possible to state that agro-pastoralists ’ experiences with herding activities provide an accumulation of a vast knowledge about forage resources15. This knowledge allows forage resources to be characterized by their potential according to a variety of criteria associated with seasonal variation and qualitative attributes, as commonly found by other studies14,15,16,17,37. Such criteria are often revealed by qualitative approaches that define the valuation perception of resources. Thus, nutritional value and palatability can be implicitly associated with the definitions of “É uma ração boa” (it is a good food), “o bicho gosta muito” (the animals like it very much) and “Rico em proteínas” (rich in protein).It should be added that the establishment of intrinsic relationships with resources allows a particular understanding at a high level of detail15,35, such as changes in palatability throughout development with descriptions including chemical17 and structural changes. Studies confirm that some Caatinga species vary in their chemical composition during leaf maturation, which influences nutritional quality17,46.In addition to revealing the domain of information, this body of knowledge allows maximizing forage use based on nutritional properties weighted by availability14,37. Nunes37 confirmed that the forage species selected by informants and the criteria they adopted coincided with nutritional values measured by the literature, and that, as also found in the present study, younger plants were recognized as highly appreciated by animals. This appreciation is due to the greater palatability of plant organs at this stage47. This is a matter of concern for the sustainability of the Caatinga, since direct or indirect grazing has compromised the regeneration process12 since younger individuals are clearly more sensitive to damage48.Also, considering the potential of Caatinga, we suggest that investment through government actions encourage the cultivation of native species to ensure the production of forage and, consequently, guarantee the sustainability of livestock activity and the ecosystem in question. More

This study proposed a protocol to evaluate the behavior of geese reared outdoors in agroforestry systems. A data collection form (i.e., BOF) was developed and validated both in relation to its reliability and its validity. In this context, moreover, ABMs useful for a welfare assessment protocol could be defined, and changes in the behavior of geese due to daily time and environmental context could be identified.Behavioral observations, based on the capture of the major changes in an animal’s body language17, are used daily in the assessment of animal health and welfare. Body language is a type of dynamic expression of the interactions among conspecifics or between animals and their environment. Behavioral changes can happen quickly or as subtle shifts not easily detectable18. Indeed, especially in the case of direct observation in the field, it becomes difficult to identify each behavioral variation. Furthermore, the on-farm use of the BOF proposed in the present study involved focal subgroup sampling, as ten geese were simultaneously observed, which may increase the difficulties. Indirect observation by videos, which allow the review of a certain action several times and the focal-animal approach, is a useful tool to partially overcome these issues and thus improve the accuracy of observation. The validation process of the BOF adopted in this study, therefore, included the definition of both its interobserver reliability and correlation with indirect observations.In this study, the direct observations in the field were performed by both an expert (i.e., main observer) and an inexperienced trained observer. As expected, the main observer was able to detect a higher frequency of behaviors, especially the rarer ones. For example, the inexperienced observer did not report any examples of allo-grooming, squawking, wagging tail, stretching, or panting behavior. However, the two observers showed excellent interobserver reliability (ICC  > 0.75). Major agreements were found for walking, roosting, and foraging. Accordingly, several studies have shown that observers with little experience can also provide a valuable contribution in observational research19,20. Overall, these results support the reliability of the BOF even if the observer’s experience helps him or her to better grasp rarer behaviors, as these behaviors could play an important role as welfare indicators.In the last two decades, important technological developments have occurred in the livestock sector. The use of sensors, cameras, and other devices can generate objective information about individual behavior, thereby allowing its evaluation in large observation areas and for large groups of animals and resulting in the better detection of natural animal behavior. Thus, in our study, the data collected by a video recording system (Noldus XT) were used as a gold standard measure to define the criterion validity of the BOF. Our results indicated excellent agreement between direct and indirect observations, supporting the BOF criterion validity. A poor correlation was only found for 2 variables (i.e., squawking and wagging tail), which were more difficult to collect by direct observation. The use of the BOF involved the simultaneous observation of 10 animals, but the geese had a synchronized behavior and moved in groups within the grazing area. This greatly facilitated focal subgroup sampling, allowed all animals to always be under observation, and could explain the high correlation between the two observation methods. However, the comparison between the observations collected in the field by the main observer and those recorded using the computerized system confirmed the greater accuracy of the latter. The analysis of the video in continuous with the use of some tools, such as the zoom or slow-motion functions, and the focal-animal sampling provided an easier identification of some behaviors and, in general, greater accuracy. Due to its nonintrusive approach, video recording has become a common practice for behavior assessment21, but it can be expensive and time-consuming. On the other hand, direct observations made by the BOF were valid and less expensive, suggesting that it could be a feasible tool with which to evaluate the welfare principle of Appropriate behavior. As recommended for welfare assessment protocols22, the BOF ethogram included indicators of both positive and negative states; however, it would be necessary to integrate it with behavioral tests and other ABMs evaluating the human-animal relationship.As mentioned above, there is no standardized geese behavior ethogram. Thus, to verify the content validity of the BOF, its behavior variables were analyzed through a PCA. The 4 extracted PCs could represent the broad behavioral dimensions of geese. In particular, the geese’s activity reported in PC1 was characterized by locomotor, foraging, and exploratory behaviors, with opposite signs with respect to roosting. The positive correlation between explorative and grazing activities and their negative correlation with static behaviors has been widely demonstrated in chickens. Chicken genotypes characterized by low exploratory aptitude exhibited low kinetic behaviors but a high frequency of roost and rest behaviors23. Göransson et al.24 showed that 50% of the observed birds exhibited sitting behavior, whereas less than 10% performed foraging activity.PC2 included all the variables that characterized the geese’s social aspects, including both positive and negative interactions. Usually, greylag geese live in a large flock because the offspring remain with their parents for an entire year. Such groups are characterized by complex relationships based on social interactions25. The formation of a group is characterized by agonistic behaviors such as fighting, pecking, and threatening, as well as submissive behaviors such as avoiding contact, crouching, and escaping26 to establish a hierarchical order. After this phase, a tolerance status develops, and birds maintain their social interactions through the use of body postures and vocalizations. Accordingly, the variables reported in PC2 were related not only to aggressive behaviors but also to geese’s vocalization and posture, which probably helped to maintain flock stability. Therefore, a higher PC2 score could indicate the need to establish and maintain a hierarchical order within the group, resulting in high social interactions.PC3 reported comfort and body care behaviors. The opportunity to spend a lot of time on body care, which should also include access to water for bathing, is of paramount importance with regard to fulfilling the biological requirements of geese27. Thus, a higher loading of this PC means that animals showed a good degree of both welfare and adaptability. In our study, a high frequency of self-cleaning and wing flapping behaviors was recorded, and the geese often took advantage of the water tub. In contrast, a very low frequency of aggression behaviors was observed, suggesting that the groups of geese were quite stable and that the animals felt safe in the environment in which they were rear. These findings confirm that agroforestry has a favorable impact on bird welfare by allowing the display of the full range of behavior, improving the animals’ comfort28.PC4 was mainly represented by the neck forward behavior. This position only occasionally represents an attack behavior and is not utilized during the establishment of hierarchical order but when it is necessary to maintain and reinforce the order inside the group. Furthermore, a goose that assumes this posture often does so while continuing another activity29. The neck forward behavior was positively associated with the stretching behavior. Stretching is usually categorized as a comfort behavior for broilers30, but it could also be used when the animal needs to relax stress-related tension in their muscles31,32 or as an adaptive strategy for dealing with unknown contexts33. Neck forward and stretching were eventually considered social avoidance behaviors, although they could be ambivalent and thus require further study, case-by-case assessment, and perhaps a better description in the ethogram.Finally, some interesting results emerged regarding the comparison of geese’s behavior during the morning and afternoon and between the two different agroforestry systems. In particular, geese showed a higher frequency of active behaviors such as walking, foraging, drinking, neck forward, and feeding during the morning compared to the afternoon. All of these behaviors suggest that geese concentrate their grazing and exploration activities during the morning. When and where to move is crucial for the food search and to avoid both predators and adverse climate conditions34. Cartoni Mancinelli et al.35 included exploratory attitude, walking, and eating grass activities in a multifactorial score as important parameters to consider to evaluate the adaptability of different organically reared chicken genotypes. Thus, exploratory and kinetic behaviors are fundamental, especially in animals reared outdoors. Moreover, the positive correlation between walking and grazing behaviors is widely known36,37. In contrast, during the afternoon, geese showed higher frequencies of static behaviors such as resting, roosting, and self-grooming, suggesting that geese are more dedicated to comfort and body care activities during this time. These trials were performed in the hottest season; thus, the geese’s behavioral differences during the day could also depend on the fact that animals preferred to carry out active behaviors during the cooler hours (morning), while in the hottest hours (afternoon), they engaged in static activities. Active behaviors cause an increase in metabolism and body temperature38, whereas static behavior, such as roosting, is considered adaptative behavior to promote heat dissipation31,39.This could also explain why higher frequencies of walking and foraging and lower frequencies of static behaviors were found in the orchard system than in the vineyard system. Studies carried out on chickens have reported that, among different pasture enrichments, the presence of trees promotes walking animal activity compared with crop inclusion40,41. The cover provided by trees made the animals feel protected from predators and provided shade during the hottest part of the day40, thereby stimulating the animals to explore all the available space in the pen. Accordingly, geese reared in the apple orchard ingested more grass than those reared in a vineyard36. However, there were no differences between the two systems for social behaviors. Moreover, the highest frequency of roosting and self-cleanliness behaviors was recorded in the vineyard, suggesting that this space offered a comfortable environment and that both systems seem respectful of the biological needs and welfare of the geese.The behavioral assessment protocol proposed in this study involving the BOF ethogram was feasible, low-cost, fast, and responsive both over time and between housing systems. It could thus be used for the assessment of Appropriate behavior in a welfare assessment protocol for geese reared in outdoor or free-range systems, although it lacks indicators of the human-animal relationship, such as avoidance distance or handling tests; such a scoring system should be developed. Regarding the specific behaviors in the two agroforestry systems, it should also be noted that they are difficult to generalize, as the characteristics of the plants, the environment, and management could have influenced these traits. Specifically, the behaviors could have been affected by the temperatures; therefore, further trials at different altitudes, seasons (i.e., autumn and winter), and climate are necessary for external validation. More

Moore, J., Pope, J., Woods, M. & Ellis, A. 2018 Aerial Survey Results: California (USDA, 2018).Stephens, S. L. et al. Drought, tree mortality, and wildfire in forests adapted to frequent fire. Bioscience 68, 77–88 (2018).Article 

Google Scholar 
Li, S. & Banerjee, T. Spatial and temporal pattern of wildfires in California from 2000 to 2019. Sci. Rep. 11, 8779 (2021).Article 

Google Scholar 
Wang, D. et al. Economic footprint of California wildfires in 2018. Nat. Sustain. 4, 252–260 (2020).Article 

Google Scholar 
Asner, G. P. et al. Progressive forest canopy water loss during the 2012–2015 California drought. Proc. Natl Acad. Sci. USA 113, E249–E255 (2016).
Google Scholar 
Brodrick, P. G., Anderegg, L. D. L. & Asner, G. P. Forest drought resistance at large geographic scales. Geophys. Res. Lett. 46, 2752–2760 (2019).Article 

Google Scholar 
Jump, A. S. et al. Structural overshoot of tree growth with climate variability and the global spectrum of drought-induced forest dieback. Glob. Change Biol. 23, 3742–3757 (2017).Article 

Google Scholar 
Goulden, M. L. & Bales, R. C. California forest die-off linked to multi-year deep soil drying in 2012–2015 drought. Nat. Geosci. 12, 632–637 (2019).Article 

Google Scholar 
Paz-Kagan, T. et al. What mediates tree mortality during drought in the southern Sierra Nevada? Ecol. Appl. 27, 2443–2457 (2017).Article 

Google Scholar 
Trugman, A. T., Anderegg, L. D. L., Anderegg, W. R. L., Das, A. J. & Stephenson, N. L. Why is Tree Drought Mortality so Hard to Predict? Trends Ecol. Evol. 36, 520–532.(2021).Goodfellow, B. W. et al. The chemical, mechanical, and hydrological evolution of weathering granitoid. J. Geophys. Res. Earth Surf. 121, 1410–1435 (2016).Article 

Google Scholar 
Shen, X., Arson, C., Ferrier, K. L., West, N. & Dai, S. Mineral weathering and bedrock weakening: modeling microscale bedrock damage under biotite weathering. J. Geophys. Res. Earth Surf. 124, 2623–2646 (2019).Article 

Google Scholar 
McLaughlin, B. C. et al. Weather underground: subsurface hydrologic processes mediate tree vulnerability to extreme climatic drought. Glob. Change Biol. 26, 3091–3107 (2020).Article 

Google Scholar 
Hahm, W. J. et al. Low subsurface water storage capacity relative to annual rainfall decouples Mediterranean plant productivity and water use from rainfall variability. Geophys. Res. Lett. 46, 6544–6553 (2019).Article 

Google Scholar 
Zhang, Y., Keenan, T. F. & Zhou, S. Exacerbated drought impacts on global ecosystems due to structural overshoot. Nat. Ecol. Evol. 5, 1490–1498 (2021).Article 

Google Scholar 
Tague, C. & Peng, H. The sensitivity of forest water use to the timing of precipitation and snowmelt recharge in the California Sierra: implications for a warming climate. J. Geophys. Res. Biogeosci. 118, 875–887 (2013).Article 

Google Scholar 
Hahm, W. J., Riebe, C. S., Lukens, C. E. & Araki, S. Bedrock composition regulates mountain ecosystems and landscape evolution. Proc. Natl Acad. Sci. USA 111, 3338–3343 (2014).Article 

Google Scholar 
Uhlig, D., Schuessler, J. A., Bouchez, J., Dixon, J. L. & von Blanckenburg, F. Quantifying nutrient uptake as driver of rock weathering in forest ecosystems by magnesium stable isotopes. Biogeosciences 14, 3111–3128 (2017).Article 

Google Scholar 
Stone, E. C. Dew as an ecological factor: II. The effect of artificial dew on the survival of Pinus ponderosa and associated species. Ecology 38, 414–422 (1957).Article 

Google Scholar 
Wald, J. A., Graham, R. C. & Schoeneberger, P. J. Distribution and properties of soft weathered bedrock at ≤1 m depth in the contiguous United States. Earth Surf. Process. Landf. 38, 614–626 (2013).Article 

Google Scholar 
Klos, P. Z. et al. Subsurface plant-accessible water in mountain ecosystems with a Mediterranean climate. WIREs Water 5, e1277 (2018).Article 

Google Scholar 
Dawson, T. E., Hahm, W. J. & Crutchfield-Peters, K. Digging deeper: what the critical zone perspective adds to the study of plant ecophysiology. N. Phytol. 226, 666–671 (2020).Article 

Google Scholar 
Rempe, D. M. & Dietrich, W. E. Direct observations of rock moisture, a hidden component of the hydrologic cycle. Proc. Natl Acad. Sci. USA 115, 2664–2669 (2018).Article 

Google Scholar 
Holbrook, W. S. et al. Links between physical and chemical weathering inferred from a 65-m-deep borehole through Earth’s critical zone. Sci. Rep. 9, 4495 (2019).Article 

Google Scholar 
Krone, L. V. et al. Deep weathering in the semi-arid Coastal Cordillera, Chile. Sci. Rep. 11, 13057 (2021).Article 

Google Scholar 
Callahan, R. P. et al. Subsurface weathering revealed in hillslope‐integrated porosity distributions. Geophys. Res. Lett. 47, e2020GL088322 (2020).Holbrook, W. S. et al. Geophysical constraints on deep weathering and water storage potential in the Southern Sierra Critical Zone Observatory. Earth Surf. Process. Landf. 39, 366–380 (2014).Article 

Google Scholar 
Hayes, J. L., Riebe, C. S., Holbrook, W. S., Flinchum, B. A. & Hartsough, P. C. Porosity production in weathered rock: where volumetric strain dominates over chemical mass loss. Sci. Adv. 5, eaao0834 (2019).Article 

Google Scholar 
Riebe, C. S. et al. Anisovolumetric weathering in granitic saprolite controlled by climate and erosion rate. Geology 49, 551–555 (2021).Article 

Google Scholar 
McCormick, E. L. et al. Widespread woody plant use of water stored in bedrock. Nature 597, 225–229 (2021).Article 

Google Scholar 
Vitousek, P. M., Porder, S. & Houlton, B. Z. Terrestrial phosphorus limitation: mechanisms, implications, and nitrogen–phosphorus interactions. Ecol. Appl. 20, 5–15 (2010).Article 

Google Scholar 
Bateman, P. C., Dodge, F. C. W. & Bruggman, P. E. Major Oxide Analyses, CPIW Norms, Modes, and Bulk Specific Gravities of Plutonic Rocks from the Mariposa 1° × 2° Sheet, Central Sierra Nevada, California Open-File Report 84–162 (USGS, 1984).Amundson, R., Richter, D. D., Humphreys, G. S., Jobbagy, E. G. & Gaillardet, J. Coupling between biota and earth materials in the critical zone. Elements 3, 327–332 (2007).Article 

Google Scholar 
Tune, A. K., Druhan, J. L., Wang, J., Bennett, P. C. & Rempe, D. M. Carbon dioxide production in bedrock beneath soils substantially contributes to forest carbon cycling. J. Geophys. Res. Biogeosci. 125, e2020JG005795 (2020).Gabet, E. J. & Mudd, S. M. Bedrock erosion by root fracture and tree throw: a coupled biogeomorphic model to explore the humped soil production function and the persistence of hillslope soils. J. Geophys. Res. 115, F04005 (2010).Bateman, P. C. Plutonism in the Central Part of the Sierra Nevada Batholith, California (USGS, 1992); http://pubs.er.usgs.gov/publication/pp1483Callahan, R. P. et al. Arrested development: erosional equilibrium in the southern Sierra Nevada, California, maintained by feedbacks between channel incision and hillslope sediment production. GSA Bull. 131, 1179–1202 (2019).Article 

Google Scholar 
Flinchum, B. A. et al. Estimating the water holding capacity of the critical zone using near-surface geophysics. Hydrol. Process. 32, 3308–3326 (2018).Article 

Google Scholar 
St. Clair, J. Geophysical Investigations of Underplating at the Middle American Trench, Weathering in the Critical Zone, and Snow Water Equivalent in Seasonal Snow. PhD thesis, Univ. Wyoming (2015).Dvorkin, J. & Nur, A. Elasticity of high‐porosity sandstones: theory for two North Sea data sets. Geophysics 61, 1363–1370 (1996).Article 

Google Scholar 
Gu, X. et al. Seismic refraction tracks porosity generation and possible CO2 production at depth under a headwater catchment. Proc. Natl Acad. Sci. USA 117, 18991–18997 (2020).Article 

Google Scholar 
Pasquet, S., Holbrook, W. S., Carr, B. J. & Sims, K. W. W. Geophysical imaging of shallow degassing in a Yellowstone hydrothermal system. Geophys. Res. Lett. 43, 12,027–12,035 (2016).Article 

Google Scholar 
Dahlgren, R. A., Boettinger, J. L., Huntington, G. L. & Amundson, R. G. Soil development along an elevational transect in the western Sierra Nevada, California. Geoderma 78, 207–236 (1997).Article 

Google Scholar 
Stone, E. L. & Kalisz, P. J. On the maximum extent of tree roots. For. Ecol. Manage. 46, 59–102 (1991).Article 

Google Scholar 
Carlson, T. N. & Ripley, D. A. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ. 62, 241–252 (1997).Article 

Google Scholar 
Goulden, M. L. et al. Evapotranspiration along an elevation gradient in California’s Sierra Nevada. J. Geophys. Res. 117, G03028 (2012).Ma, Q. et al. Wildfire controls on evapotranspiration in California’s Sierra Nevada. J. Hydrol. 590, 125364 (2020).Article 

Google Scholar 
Roche, J. W., Goulden, M. L. & Bales, R. C. Estimating evapotranspiration change due to forest treatment and fire at the basin scale in the Sierra Nevada, California. Ecohydrology 11, e1978 (2018).Bales, R. C. et al. Mechanisms controlling the impact of multi-year drought on mountain hydrology. Sci. Rep. 8, 690 (2018).Article 

Google Scholar 
Roy, D. P. et al. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sens. Environ. 185, 57–70 (2016).Article 

Google Scholar 
Su, Y. et al. Emerging stress and relative resiliency of giant sequoia groves experiencing multiyear dry periods in a warming climate. J. Geophys. Res. Biogeosci. 122, 3063–3075 (2017).Article 

Google Scholar 
Moore, J., McAfee, L. & Iaccarino, J. 2016 Aerial Survey Results: California (USDA, 2017).Budyko, M. I., Miller, D. H. & Miller, D. H. Climate and Life (Academic Press, 1974).Hargreaves, G. H. & Samani, Z. A. Reference crop evapotranspiration from temperature. Appl. Eng. Agric. 1, 96–99 (1985).Article 

Google Scholar 
PRISM Climate Group PRISM Climate Data (Oregon State Univ., 2019).Bales, R. et al. Spatially distributed water-balance and meteorological data from the rain–snow transition, southern Sierra Nevada, California. Earth Syst. Sci. Data 10, 1795–1805 (2018).Article 

Google Scholar 
Callahan, R. P. Supplement for “Forest vulnerability to drought controlled by bedrock composition”. Hydroshare https://doi.org/10.4211/hs.edbb6ebfbc744186b5800932cd00b507 (2022).Earth Resources Observation and Science (EROS) Center USGS EROS Archive—Aerial Phorography—National Agriculture Imagery Program (NAIP) (USGS, 2017); https://doi.org/10.5066/F7QN651G More

Bedrock composition can play a critical role in determining the structure and water demand of forests, influencing their vulnerability to drought. The properties of bedrock can help explain within-region patterns of tree mortality in the 2011–2017 California drought.Montane forests are iconic natural resources that provide habitat, carbon sequestration, regulation of water, and, for many cultures, profound meaning. A warming climate and prolonged droughts threaten these forests, as shown by the 2011–2017 drought in California, USA, which killed over 140 million trees. However, the vulnerability of forests to climate-driven risks is not evenly distributed across these landscapes. In the 2011–2017 drought, some contiguous forested areas (or forest stands) suffered more than 70% mortality while forests in other locations experienced few or no losses1. Understanding these spatial patterns is critical for the projection of future risks and for targeted forest management. Writing in Nature Geoscience, Callahan and colleagues look beneath the surface at the composition of bedrock and find a link to these patterns of drought mortality in the California Sierra2. More

Hughes, T. P. & Jackson, J. B. C. Do corals lie about their age? Some demographic consequences of partial mortality, fission and fusion. Science 209, 713–715 (1980).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Hughes, R. N. A. Functional Biology of Clonal Animals 331 (Chapman and Hall, 1989).
Google Scholar 
Karlson, R. H. Fission and the dynamics of genets and ramets in clonal cnidarian populations. Hydrobiologia 216, 235–240 (1991).Article 

Google Scholar 
Hughes, T. P. & Jackson, J. B. C. Population dynamics and life histories of foliaceous corals. Ecol. Monogr. 55(2), 141–166 (1985).Article 

Google Scholar 
Blanquer, A., Uriz, M. J. & Caujapé-Castells, J. Small-scale spatial genetic structure in Scopalina lophyropoda, an encrusting sponge with philopatric larval dispersal and frequent fission and fusion events. Mar. Ecol. Prog. Ser. 380, 95–102 (2009).Article 
ADS 

Google Scholar 
Bely, A. E. & Wray, G. A. Evolution of regeneration and fission in annelids: Insights from engrailed- and orthodenticle-class gene expression. Development 128, 2781–2791 (2001).CAS 
PubMed 
Article 

Google Scholar 
Burton, P. M. & Finnerty, J. R. Conserved and novel gene expression between regeneration and asexual fission in Nematostella vectensis. Dev. Genes Evol. 219, 79–87 (2009).PubMed 
Article 

Google Scholar 
Zattara, E. E. & Bely, A. E. Phylogenetic distribution of regeneration and asexual reproduction in Annelida: Regeneration is ancestral and fission evolves in regenerative clades. Invertebr. Biol. 135(4), 400–414 (2016).Article 

Google Scholar 
Dolmatov, I. Y., Afanasyev, S. V. & Boyko, A. V. Molecular mechanisms of fission in echinoderms: Transcriptome analysis. PLoS ONE 13(4), 0195836 (2018).Article 
CAS 

Google Scholar 
Jackson, J. B. C. & Hughes, T. P. Adaptive strategies of coral-reef invertebrates. Am. Sci. 73(3), 265–274 (1985).ADS 

Google Scholar 
Garrabou, J. Life-history traits of Alcyonium acaule and Parazoanthus axinellae (Cnidaria, Anthozoa), with emphasis on growth. Mar. Ecol. Prog. Ser. 178, 193–204 (1999).Article 
ADS 

Google Scholar 
Elahi, R. & Edmunds, P. J. Consequences of fission in the coral Siderastrea siderea: Growth rates of small colonies and clonal input to population structure. Coral Reefs 26(2), 271–276 (2007).Article 
ADS 

Google Scholar 
Jackson, J. B. C., Thorp, J. H. & Gibbons, J. W. Overgrowth competition between encrusting cheilostome ectoprocts in a Jamaican cryptic reef environment. J. Anim. Ecol. 48, 805–823 (1979).Article 

Google Scholar 
Karlson, R. H. Dynamics of Coral Communities. Population and Community Biology Series Vol. 23, 1–250 (Kluwer Academic Publishers, 1999).Book 

Google Scholar 
Acosta, A., Sammarco, P. W. & Duarte, L. F. New fission processes in the zoanthid Palythoa caribaeorum: Description and quantitative aspects. Bull. Mar. Sci. 76(1), 1–26 (2005).
Google Scholar 
Babcock, R. C. Comparative demography of three species of scleractinian corals using age- and size-dependent classifications. Ecol. Monogr. 61(3), 225–244 (1991).Article 

Google Scholar 
Tanner, J. E. The influence of clonality on demography: Patterns in expected longevity and survivorship. Ecology 82(7), 1971–1981 (2001).Article 

Google Scholar 
Linacre, N. A. & Keough, M. J. Demographic effects of fragmentation history in modular organisms: Illustrated using the bryozoan Mucropetraliella ellerii (MacGillivray). Ecol. Model. 170(1), 61–71 (2003).Article 

Google Scholar 
Brito-Millán, M., Vermeij, M. J., Alcantar, E. A. & Sandin, S. A. Coral reef assessments based on cover alone mask active dynamics of coral communities. Mar. Ecol. Prog. Ser. 630, 55–68 (2019).Article 
ADS 

Google Scholar 
Geller, J. B., Fitzgerald, L. J. & King, C. E. Fission in sea anemones: Integrative studies of life cycle evolution. Integr. Comp. Biol. 45(4), 615–622 (2005).CAS 
PubMed 
Article 

Google Scholar 
Hunter, T. The energetics of asexual reproduction: Pedal laceration in the symbiotic sea anemone Aiptasia pulchella (Carlgren, 1943). J. Exp. Mar. Biol. Ecol. 83(2), 127–147 (1984).Article 

Google Scholar 
Bak, R. P. M., Sybesma, J. & Van Duyl, F. C. The ecology of the tropical compound ascidian Trididemnum solidum. II. Abundance, growth and survival. Mar. Ecol. Prog. Ser. 6, 43–52 (1981).Article 
ADS 

Google Scholar 
Rinkevich, B. & Weissman, I. L. A long-term study of fused subclones of a compound ascidian. The resorption phenomenon. J. Zool. 213, 717–733 (1987).Article 

Google Scholar 
Stoner, D. S. Fragmentation: A mechanism for the stimulation of the genet growth rates in an encrusting colonial ascidian. Bull. Mar. Sci. 45, 277–287 (1989).ADS 

Google Scholar 
Lambert, G. Ecology and natural history of the protochordates. Can. J. Zool. 83(1), 34–50 (2005).Article 

Google Scholar 
López-Legentil, S., Erwin, P. M., Velasco, M. & Turon, X. Growing or reproducing in a temperate sea: Optimization of resource allocation in a colonial ascidian. Invertebr. Biol. 132(1), 69–80 (2013).Article 

Google Scholar 
Fidler, A. E., Bacq-Labreuil, A., Rachmilovitz, E. N. & Rinkevich, B. Efficient dispersal and substrate acquisition traits in a marine invasive species via transient chimerism and colony mobility. Peer J. 6, e5006 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Grosberg, R. K. Life-history variation within a population of the colonial ascidian Botryllus schlosseri. 1. The genetic and environmental control of seasonal variation. Evolution 42, 900–920 (1988).PubMed 

Google Scholar 
Stocker, L. J. & Underwood, A. J. The relationship between the presence of neighbours and rates of sexual and asexual reproduction in a colonial invertebrate. J. Exp. Mar. Biol. Ecol. 149(2), 191–205 (1991).Article 

Google Scholar 
Reem, E., Douek, J., Paz, G., Katzir, G. & Rinkevich, B. Phylogenetics biogeography and population genetics of the ascidian Botryllus schlosseri in the Mediterranean Sea and beyond. Mol. Phylogenet. Evol. 107, 221–231 (2017).PubMed 
Article 

Google Scholar 
Reem, E., Douek, J. & Rinkevich, B. A critical deliberation of the “species complex” status of the globally-spread colonial ascidian Botryllus schlosseri. J. Mar. Biol. Ass. UK in press (2022).Rinkevich, B. Senescence in Modular Animals—Botryllid Ascidians as a Unique Aging System. In The Evolution of Senescence in the Tree of Life (eds Salguero-Gomez, R. et al.) 220–237 (Cambridge University Press, 2017).Chapter 

Google Scholar 
Rinkevich, B. & Shapira, M. An improved diet for inland broodstock and the establishment of an inbred line from Botryllus schlosseri, a colonial sea squirt (Ascidiacea). Aquat. Living Resour. 11(3), 163–171 (1998).Article 

Google Scholar 
Manni, L. et al. Sixty years of experimental studies on the blastogenesis of the colonial tunicate Botryllus schlosseri. Dev. Biol. 448(2), 293–308 (2018).PubMed 
Article 
CAS 

Google Scholar 
Ben-Hamo, O., Rosner, A., Rabinowitz, C., Oren, M. & Rinkevich, B. Coupling astogenic aging in the colonial tunicate Botryllus schlosseri with the stress protein mortalin. Dev. Biol. 433(1), 33–46 (2018).CAS 
PubMed 
Article 

Google Scholar 
Rinkevich, B. & Weissman, I. L. The fate of Botryllus (Ascidiacea) larvae cosettled with parental colonies: Beneficial or deleterious consequences?. Biol. Bull. 173, 474–488 (1987).PubMed 
Article 

Google Scholar 
Rinkevich, B., Porat, R. & Goren, M. On the development and reproduction of Botryllus schlosseri (Tunicata) colonies from the eastern Mediterranean Sea: Plasticity of life history traits. Invertebr. Reprod. Dev. 34, 207–218 (1998).Article 

Google Scholar 
Rinkevich, B., Porat, R. & Goren, M. Ecological and life history characteristics of Botryllus schlosseri (Tunicata) populations inhabiting undersurface shallow water stones. Mar. Ecol. 19, 129–145 (1998).Article 
ADS 

Google Scholar 
Rinkevich, B. & Weissman, I. L. Retreat Growth in the Ascidian Botryllus schlosseri. The Consequences of Non-self Recognition. In Invertebrate Historecognition (ed. Grosberg, R. K.) 93–109 (Plenum Press, 1988).Chapter 

Google Scholar 
Voskoboynik, A., Reznick, A. Z. & Rinkevich, B. Rejuvenescence and extension of an urochordate life span following a single, acute administration of an anti-oxidant, butylated hydroxytoluene. Mech. Ageing Dev. 123, 1203–1210 (2002).CAS 
PubMed 
Article 

Google Scholar 
Stearns, S. C. The Evolution of Life Histories (Oxford University Press, 1992).
Google Scholar 
Stearns, S. C. Life history evolution: Successes, limitations, and prospects. Naturwissenschaften 87(11), 476–486 (2000).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Healy, K., Ezard, T. H., Jones, O. R., Salguero-Gómez, R. & Buckley, Y. M. Animal life history is shaped by the pace of life and the distribution of age-specific mortality and reproduction. Nat. Ecol. Evol. 3(8), 1217–1224 (2019).PubMed 
Article 

Google Scholar 
Engelen, A. H., Breeman, A. M., Olsen, J. L., Stam, W. T. & Åberg, P. Life history flexibility allows Sargassum polyceratium to persist in different environments subjected to stochastic disturbance events. Coral Reefs 24(4), 670–680 (2005).Article 
ADS 

Google Scholar 
Lailvaux, S. P. & Husak, J. F. The life history of whole-organism performance. Q. Rev. Biol. 89(4), 285–318 (2014).PubMed 
Article 

Google Scholar 
Christie, M. R., McNickle, G. G., French, R. A. & Blouin, M. S. Life history variation is maintained by fitness trade-offs and negative frequency-dependent selection. Proc. Natl. Acad. Sci. USA 115(17), 4441–4446 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Reid, J. M. et al. Parent age, lifespan and offspring survival: Structured variation in life history in a wild population. J. Anim. Ecol. 79(4), 851–862 (2010).PubMed 

Google Scholar 
Steiner, U. K., Tuljapurkar, S. & Orzack, S. H. Dynamic heterogeneity and life history variability in the kittiwake. J. Anim. Ecol. 79(2), 436–444 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
Beauplet, G., Barbraud, C., Dabin, W., Kussener, C. & Guinet, C. Age specific survival and reproductive performances in fur seals: Evidence of senescence and individual quality. Oikos 112, 430–441 (2006).Article 

Google Scholar 
Salguero-Gómez, R. et al. Fast–slow continuum and reproductive strategies structure plant life-history variation worldwide. Proc. Natl. Acad. Sci. USA 113(1), 230–235 (2016).PubMed 
Article 
ADS 
CAS 

Google Scholar 
Pianka, E. R. On r and K selection. Am. Nat. 104(940), 592–597 (1970).Article 

Google Scholar 
Hughes, P. W. Between semelparity and iteroparity: Empirical evidence for a continuum of modes of parity. Ecol. Evol. 7(20), 8232–8261 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Simpson, C. An ecological driver for the macroevolution of morphological polymorphism within colonial invertebrates. J. Exp. Zool. B Mol. Dev. Evol. 336(3), 231–238 (2021).PubMed 
Article 

Google Scholar 
Sæther, B. E., Ringsby, T. H. & Roskaft, E. Life-history variation, population processes and priorities in species conservation: Towards a reunion of research paradigms. Oikos 77, 217–226 (1996).Article 

Google Scholar 
Monro, K. & Marshall, D. J. Faster is not always better: Selection on growth rate fluctuates across life history and environments. Am. Nat. 183(6), 798–809 (2014).PubMed 
Article 

Google Scholar 
Kaliszewicz, A., Johst, K., Grimm, V. & Uchmański, J. Predation effects on the evolution of life-history traits in a clonal oligochaete. Am. Nat. 166(3), 409–417 (2005).PubMed 
Article 

Google Scholar 
Herrera-Cubilla, A., Dick, M. H., Sanner, J. & Jackson, J. B. C. Neogene Cupuladriidae of tropical America. I: Taxonomy of recent Cupuladria from opposite sides of the Isthmus of Panama. J. Paleontol. 80, 245–263 (2006).Article 

Google Scholar 
Bingham, B. L., Dimond, J. L. & Muller-Parker, G. Symbiotic state influences life-history strategy of a clonal cnidarian. Proc. R. Soc. B 281(1789), 20140548 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Chalmandrier, L. et al. Linking functional traits and demography to model species-rich communities. Nat. Commun. 12, 2724 (2021).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Rüger, N. et al. Demographic trade-offs predict tropical forest dynamics. Science 368, 165–168 (2020).PubMed 
Article 
ADS 
CAS 

Google Scholar 
Ben-Shlomo, R. Invasiveness, chimerism and genetic diversity. Mol. Ecol. 26, 6502–6509 (2017).CAS 
PubMed 
Article 

Google Scholar 
Watanabe, H. Studies on the regulation in fused colonies in Botryllus primigenus (Ascidiae Compositae). Sci. Rep. Tokyo Bunrika Daigaku Sect. B 7, 183–198 (1953).
Google Scholar 
Lauzon, R. J., Rinkevich, B., Patton, C. W. & Weissman, I. L. A morphological study of non-random senescence in a colonial urochordate. Biol. Bull. 198, 367–378 (2000).CAS 
PubMed 
Article 

Google Scholar  More

Effects of Se and K-humate on nitrogen concentrationsThe N concentration in the soil varied in availability and total content in oat straw and seeds after the foliar application of Se and K-humate. Se alone increased the availability of N in the soil in the following order: Se3  > Se2  > Se1  > control. Thus, Se was found to increase the available N-soil in an application-rate-dependent manner (Table 2). The availability of N-soil after Se application was improved via the simultaneous application of K-humate with the same rate-dependence as observed with Se alone. Comparable results were found using the sum of means for analysis. The insignificant difference found between the sum of means for control and treatment at an Se concentration of 12 × 10−3 mM Se may reflect the relatively low concentration of Se used.Table 2 Effect of selenium and K-humate on nitrogen content.Full size tableThe total N-straw content increased as a result of an increased content of N-plant (Table 2). Differences were found to be insignificant between Se concentrations of 12 × 10−3 mM, 63 × 10−3 mM, and controls. Likewise, the simultaneous application of K-humate showed insignificant differences between Se concentrations of 63 × 10−3 mM and 88 × 10−3 mM. Insignificant differences were noted between the control and Se concentration of 12 × 10−3 mM and the Se concentration of 63 × 10−3 and 88 × 10−3 mM using the sum of means. The total N-seeds content increased for application rates of 12 × 10−3–88 × 10−3 mM, and the simultaneous application of K-humate augmented this increase. The application rate dependency of the effects of Se and K-humate application was identical to that observed in N-soil and N-straw. No significant differences among Se and K-humate applications were observed. An insignificant difference was observed among the sum of means for Se and K-humate applications at concentrations of 63 × 10−3 and 88 × 10−3 mM.The application of Se caused proportional increases in N-soil, N-straw, and N-seeds, and the simultaneous application of K-humate improved this effect. Previously, the application of Se resulted in an increase in the accumulation of NPK which altered N and K distribution. However, the distribution of P was not affected19. Furthermore, the application of Se ultimately resulted in an increase in the accumulation of N, calcium (Ca), K, and Mn20. A significant increase in concentrations of N and S in the rice grain plants grown under N-limiting conditions was also observed while the Ca that have been treated with Se regardless of N supply21. Thus, a synergistic interaction between Se and N in total grain proteins was reported21.Effects of Se and K-humate on PThe effect of applications of different Se concentrations without K-humate on the available P-soil showed a reduction in the following order: Se3  > Se2  > Se1  > control (Table 3). Thus, the foliar application rate of Se caused a rate-dependent increase in the available P-soil. Simultaneous application of K-humate further increased P-soil availability. A rate dependency similar to Se alone was also observed with simultaneous Se and K-humate application. A similar result was observed using the sum of means for data analysis. Significant differences were observed among all treatments.Table 3 Effect of selenium and K-humate on phosphorous content.Full size tableFoliar application of Se increased total P-straw. An insignificant difference was found between the control and Se concentrations of 12 × 10−3 and 63 × 10−3 mM, which was similar to findings observed after the application of K-humate. Moreover, insignificant differences were observed between the applications of Se and Se + K-humate. An insignificant effect was found between control and Se concentrations of (12 × 10−3 and 63 × 10−3 mM), and K-humate application using the sum of means.The application of Se having concentrations ranging from 12 × 10−3 to 88 × 10−3 mM resulted in increased P-seeds and the addition of K-humate augmented this effect (Table 3). The effect of Se and K-humate applications showed a decrease in the following order: Se3  > Se2  > Se1  > control. Insignificant differences between values were observed when Se was applied without K-humate at concentrations of 12 × 10−3 and 63 × 10−3 mM, and for the sum of means for Se and K-humate applications at concentrations of 12 × 10−3 and 63 × 10−3 mM. Thus, the application rate of Se caused a proportional increase in P-soil, P-straw, and P-seeds. Furthermore, the simultaneous application of K-humate augmented this effect.Consistently, concentrations of P and Ca increased in response to the application of selenite-Se (Na2SeO3⋅5H2O) to maize seedlings22, and the application of Se led to an increase in the accumulation of NPK, with alteration of N and K distribution. However, the distribution of P was not influenced19.Effects of the foliar application of Se and K-humate on KDifferent application rates of Se without humate increased K-soil and this effect showed a decrease in the following order: Se3  > Se2  > Se1 = control (Table 4). Again, the foliar application rate of Se causes a proportional increase, in this case, in K-soil. The application of K-humate with Se augmented this effect. A similar rate dependency was also observed with simultaneous application and when the sum of means was used. An insignificant difference was observed between the sum of means for controls and Se concentrations of 12 × 10−3 mM.Table 4 Effect of selenium and K-humate on potassium content.Full size tableThe foliar application of Se led to a slight increase in the total K-straw content (Table 4). An insignificant change was observed for Se concentrations from 12 × 10−3 to 88 × 10−3 mM, and similar results were found with the additional application of K-humate.The application of Se at concentrations from 12 × 10−3 to 88 × 10−3 mM resulted in a slight increase in K-seeds, and the additional application of K-humate only slightly increased the accumulation of K (Table 4). An insignificant difference was observed between Se alone and with K-humate. Similar findings were noted when the sum of means was used for analysis. Se application rates thus produce a proportional increase in K-soil but not in K-straw or K-seeds. Comparable data were noted after K-humate addition. Concentrations of K previously decreased in response to selenite-Se (Na2SeO3⋅5H2O) application to maize seedlings; however, magnesium (Mg) concentrations did not change22. Moreover, the application of Se led to the accumulation of NPK and altered N and K distribution without affecting the P distribution19. Consistently, the application of Se ultimately resulted in increasing K accumulation20.Effects of Se and K-humate application on oat growthApplication of Se improved the yield, which was assessed as kg × 10−3/feddan (Table 5). Higher concentrations of Se produced a higher yield of oat. The effect of Se showed a reduction in the following order: Se3  > Se2  > Se1  > control. The simultaneous application of K-humate increased the yield only slightly, resulting in insignificant differences. Similar findings were also observed when the sum of means was used. In contrast, seed production was not significantly affected, and plant length (m × 10–2) did not show a significant response. In contrast, Se application to potato plants enhanced tuber yield, plant growth, and quality compared with controls. Moreover, Se application along with different N additions ultimately increased potato productivity compared with Se or N alone23. Similarly, the grain yield increased when Se was applied; this application was significant at low levels24.Table 5 Effect of Se and K-humate application on oat growth.Full size tableEffects of Se and K-humate applications on OMS (%) and non-enzymatic antioxidants and total phenols in oat plantsThe total OMS content increased with increasing Se concentrations, perhaps due to stimulation of root growth or microbial biomass. This effect showed a decrease in the following order: Se3  > Se2  > Se1  > control. The addition of K-humate by foliar application significantly augmented the OMS content (%) (Table 6). Application of Se also increased the non-enzymatic antioxidant content; however, the increases were insignificant at Se concentrations of 12 × 10−3 and 63 × 10−3 mM. The highest values for non-enzymatic antioxidants were observed at Se concentrations of 88 × 10−3 mM. The application of K-humate along with Se did not significantly augment the effects observed after the application of Se alone. Analyses using the sum of means were completely consistent with these findings.Table 6 Effect of selenium and K-humate application on organic matter in soil (OMS), non-enzymatic antioxidant, and total phenols in oats.Full size tableSe positively enhanced the total phenol content with effects decreasing in the following order: Se3  > Se2  > Se1  > control. Furthermore, this effect was significantly amplified with the simultaneous application of K-humate. Analysis using the sum of means gave comparable results. Se enhances the ability of plants to cope with stress by stimulating plant cell antioxidant capacity though the upregulating of antioxidant enzymes, such as CAT, SOD, and GSH-Px. Se also increases the synthesis of PCs, GSH, proline, ascorbate, alkaloids, flavonoids, and carotenoids. Se may also induce the spontaneous dismutation of the superoxide radical into H2O2. Elevated antioxidant capacity can reduce lipid peroxidation by lowering ROS accumulation under metal-induced oxidative stress conditions25. Application of Se using foliar spray also induced an increase in the concentration of rosmarinic acid20.Effects of Se and K-humate applications on Se contentAfter the application of Se, Se-soil concentrations increased. The effects of Se concentrations decreased in the following order: Se3  > Se2  > Se1  > control. The additional application of K-humate significantly amplified these effects (Table 7). The treatment of K-humate that increased Se content in the soil may be owing to experimental errors, however, increasing Se content in either straw or seeds may be owing to the increased stimulating movement from soil to different parts of the plant. Se-straw content increased with increasing the Se foliar application; this effect decreased in the following order: Se3  > Se2  > Se1  > control. The simultaneous application of K-humate augmented the effects observed after the application of Se alone. Total Se concentration also increased Se-seeds like Se-straw for Se alone, Se with K-humate, and using the sum of means for analysis.Table 7 Effects of Se and K-humate applications on Se content.Full size tableEffects of Se and K-humate application on Cr contentThe highest concentrations of Cr were observed in control plants followed by Se2  > Se3  > Se1. In response to Se application, the Cr-straw content decreased (Table 8). The difference between Se2 and Se3 was insignificant. K-humate addition induced a notable increase in Cr-straw in the following order: control  > Se3  > Se2  > Se1. This may be owing to the increased stimulating movement of Cr from soil to different parts of the plant. Results obtained from Se treatments varied depending on the presence of K-humate. Cr-seeds decreased in the following order: Se2  > Se3  > Se2  > control. The addition of K-humate increased the Cr-seed content compared with Se alone; however, the difference between Se2 and Se3 was insignificant. Analysis using the sum of means did not produce significant differences.Table 8 Effects of Se and K-humate application on Cr content.Full size tableEffects of Se and K-humate applications on Fe contentVariable effects were produced using different application rates of Se on Fe-straw, and this effect was observed in the following order: Se3  > Se1  > control  > Se2 (Table 9). Differences were insignificant among control, Se1, and Se2. K-humate caused concentrations of Fe-straw to significantly increase in the following order: control  > Se3  > Se2  > Se1. Differences between control and Se3 as well as Se1 and Se2 were insignificant. Analysis using the sum of means was similar. Neither Se nor Se with K-humate applications produced significant changes in Fe-seeds. Analysis using the sum of means was similar. Low concentration of Se application may enhance plant productivity and encourage phytoremediation by improving plant tolerance to stress and enhancing photosynthesis25. Further, a significant increase was observed in concentrations of Fe and S in rice grain grown in N-limiting conditions while Ca that have been treated with Se regardless of N supply21.Table 9 Effects of Se and K-humate applications on Fe content.Full size tableEffects of Se and K-humate application on Mn contentApplication of Se reduced the Mn-straw content, and this effect was observed in the following order: control  > Se2  > Se1  > Se3. No significant difference was found between control and Se1 (Table 10). In contrast, K-humate addition further reduced Mn-straw concentrations in the following order: control  > Se1  > Se3  > Se2. The control and Se1 were not significantly different when using the sum of means for analysis. Likewise, no significant difference was seen between Se1 and Se3. Accumulation of Mn in seeds varied among treatments in the following order: control  > Se2  > Se3  > Se1. K-humate addition altered this order to be in the following order: control  > Se2  > Se1  > Se3. No significant differences were observed between Se2 and Se3 when the sum of means for analysis was used. Previously, the application of Se increased the concentrations of Mg and molybdenum in grains grown in 16 and 24 mM N compared with N-limited plants21.Table 10 Effects of Se and K-humate application on Mn content.Full size tableEffect of Se and K-humate applications on Zn content in oat plantsApplication of Se2—the middle concentration of Se—resulted in highest accumulation in Zn-straw, and this effect was observed in the following order: Se2  > Se1  > control  > Se3 (Table 11). The application of K-humate with Se resulted in some insignificant variations compared with the application of Se alone. Control, Se1, and Se3 were insignificantly different when the sum of means was used for the analysis. Concentrations of Zn in seeds were reduced after Se application. K-humate with Se foliar application altered the concentration of Zn in seeds with impacts in the following order: control  > Se3  > Se1  > Se2. The difference between Se1 and Se3 was insignificant. Additionally, insignificant differences in Zn concentrations after application of Se1, Se2, and Se3 were found when the sum of means was used for analysis. Low concentrations of Se possibly enhance plant productivity and phytoremediation capacity by improving the ability of plants to tolerate stress and enhancing photosynthesis25.Table 11 Effect of Se and K-humate applications on Zn containing oat plant.Full size tableEffects of Se and K-humate application on Cu contentIncreasing concentrations of Se from 12 × 10−3 to 88 × 10−3 mM increased the concentration of Cu-seed, and this effect was observed in the following order: Se1  > control  > Se2  > Se3 as it shown in Table 12. Application of Se with K-humate showed significant changes in the Cu-straw content in the following order: Se1  > Se2  > control  > Se3. No significant differences were observed using the sum of means for analyses. In contrast, the foliar application of Se resulted in increases in Cu-seed at concentrations of Se1 and Se3; however, at 63 × 10−3 mM (Se2), a reduction in Cu-seed was observed. K-humate with Se simultaneously resulted in increased Cu-seed content with impacts decreasing in the following order: Se3  > Se1  > control  > Se2. The sum of means analysis showed no significant variation between control and Se2. Previously, the application of Se led to a decrease in the concentrations of Cu in grains grown in 16 and 24 mm N compared with N-limited plants21.
Table 12 Effects of Se and K-humate application on Cu content.Full size table More

Murray, N. J. et al. The global distribution and trajectory of tidal flats. Nature 565, 222–225, https://doi.org/10.1038/s41586-018-0805-8 (2019).CAS 
Article 
PubMed 

Google Scholar 
Bishop, M. J., Murray, N. J., Swearer, S. & Keith, D. A. In The IUCN Global Ecosystem Typology 2.0: Descriptive profiles for biomes and ecosystem functional groups (eds D. A. Keith, J. R. Ferrer-Paris, E. Nicholson, & R. T. Kingsford) (IUCN, 2020).Keith, D. A. et al. Earth’s ecosystems: a function-based typology for conservation and sustainability. Nature (In review).Murray, N. J., Phinn, S. R., Clemens, R. S., Roelfsema, C. M. & Fuller, R. A. Continental scale mapping of tidal flats across East Asia using the Landsat Archive. Remote Sensing 4, 3417–3426, https://doi.org/10.3390/Rs4113417 (2012).Article 

Google Scholar 
Murray, N. J., Clemens, R. S., Phinn, S. R., Possingham, H. P. & Fuller, R. A. Tracking the rapid loss of tidal wetlands in the Yellow Sea. Fron. Ecol. Environ. 12, 267–272, https://doi.org/10.1890/130260 (2014).Article 

Google Scholar 
Murray, N. J., Ma, Z. & Fuller, R. A. Tidal flats of the Yellow Sea: A review of ecosystem status and anthropogenic threats. Austral Ecol. 40, 472–481, https://doi.org/10.1111/aec.12211 (2015).Article 

Google Scholar 
Dhanjal-Adams, K. et al. Distribution and protection of intertidal habitats in Australia. Emu 116, 208–214 (2015).Article 

Google Scholar 
Murray, N. J. et al. High-resolution mapping of losses and gains of Earth’s tidal wetlands. Science 376, 744–749, https://doi.org/10.1126/science.abm9583 (2022).CAS 
Article 
PubMed 

Google Scholar 
Gong, P. et al. Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data. Int. J. Remote Sens. 34, 2607–2654 (2013).Article 

Google Scholar 
Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031 (2017).Article 

Google Scholar 
Turner, W. et al. Free and open-access satellite data are key to biodiversity conservation. Biol. Conserv. 182, 173–176 (2015).Article 

Google Scholar 
Murray, N. J. et al. The role of satellite remote sensing in structured ecosystem risk assessments. Sci Total Environ 619–620, 249–257, https://doi.org/10.1016/j.scitotenv.2017.11.034 (2018).CAS 
Article 
PubMed 

Google Scholar 
Ying, Q. et al. Global bare ground gain from 2000 to 2012 using Landsat imagery. Remote Sens. Environ. 194, 161–176, https://doi.org/10.1016/j.rse.2017.03.022 (2017).Article 

Google Scholar 
Song, X.-P. et al. Global land change from 1982 to 2016. Nature 560, 639–643, https://doi.org/10.1038/s41586-018-0411-9 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Noble, S. et al. A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units AU – Sayre, Roger. Journal of Operational Oceanography, 1–10, https://doi.org/10.1080/1755876X.2018.1529714 (2018).Sayre, R. et al. A global ecological classification of coastal segment units to complement marine biodiversity observation network assessments. Oceanography 34, 120–129 (2021).Article 

Google Scholar 
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853, https://doi.org/10.1126/science.1244693 (2013).CAS 
Article 
PubMed 

Google Scholar 
Margono, B. A., Potapov, P. V., Turubanova, S., Stolle, F. & Hansen, M. C. Primary forest cover loss in Indonesia over 2000–2012. Nature Climate Change 4, 730–735, https://doi.org/10.1038/nclimate2277 (2014).Article 

Google Scholar 
Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111, https://doi.org/10.1126/science.aau3445 (2018).CAS 
Article 
PubMed 

Google Scholar 
Pekel, J. F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422, https://doi.org/10.1038/nature20584 (2016).CAS 
Article 
PubMed 

Google Scholar 
Pickens, A. H. et al. Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series. Remote Sens. Environ. 243, 111792, https://doi.org/10.1016/j.rse.2020.111792 (2020).Article 

Google Scholar 
Yamazaki, D., Trigg, M. A. & Ikeshima, D. Development of a global ~ 90 m water body map using multi-temporal Landsat images. Remote Sens. Environ. 171, 337–351, https://doi.org/10.1016/j.rse.2015.10.014 (2015).Article 

Google Scholar 
Fluet-Chouinard, E., Lehner, B., Rebelo, L.-M., Papa, F. & Hamilton, S. K. Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data. Remote Sens. Environ. 158, 348–361, https://doi.org/10.1016/j.rse.2014.10.015 (2015).Article 

Google Scholar 
Bunting, P. et al. The Global Mangrove Watch—A new 2010 global baseline of mangrove extent. Remote Sensing 10, 1669 (2018).Article 

Google Scholar 
Worthington, T. A. et al. Harnessing Big Data to Support the Conservation and Rehabilitation of Mangrove Forests Globally. One Earth 2, 429–443, https://doi.org/10.1016/j.oneear.2020.04.018 (2020).Article 

Google Scholar 
Worthington, T. A. et al. A global typology of mangroves and its relevance for ecosystem services and deforestation. Scientific reports (2020).Thomas, N. et al. Distribution and drivers of global mangrove forest change, 1996–2010. PLOS ONE 12, e0179302, https://doi.org/10.1371/journal.pone.0179302 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Simard, M. et al. Mangrove canopy height globally related to precipitation, temperature and cyclone frequency. Nature Geoscience 12, 40–45, https://doi.org/10.1038/s41561-018-0279-1 (2019).CAS 
Article 

Google Scholar 
Allen, G. H. & Pavelsky, T. M. Global extent of rivers and streams. Science 361, 585–588, https://doi.org/10.1126/science.aat0636 (2018).MathSciNet 
CAS 
Article 
PubMed 
MATH 

Google Scholar 
Lyons, M. et al. Mapping the world’s coral reefs using a global multiscale earth observation framework. Remote Sensing in Ecology and Conservation (2020).Li, J. et al. A global coral reef probability map generated using convolutional neural networks. Coral Reefs https://doi.org/10.1007/s00338-020-02005-6 (2020).Article 

Google Scholar 
Yang, X., Pavelsky, T. M. & Allen, G. H. The past and future of global river ice. Nature 577, 69–73, https://doi.org/10.1038/s41586-019-1848-1 (2020).CAS 
Article 
PubMed 

Google Scholar 
Newbold, T. et al. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science 353, 288–291, https://doi.org/10.1126/science.aaf2201 (2016).CAS 
Article 
PubMed 

Google Scholar 
Tittensor, D. P. et al. A mid-term analysis of progress toward international biodiversity targets. Science 346, 241–244, https://doi.org/10.1126/science.1257484 (2014).CAS 
Article 
PubMed 

Google Scholar 
Lee, C. K. F., Nicholson, E., Duncan, C. & Murray, N. J. Estimating changes and trends in ecosystem extent with dense time-series satellite remote sensing. Conserv Biol 35, 325–335, https://doi.org/10.1111/cobi.13520 (2021).Article 
PubMed 

Google Scholar 
Deegan, L. A. et al. Coastal eutrophication as a driver of salt marsh loss. Nature 490, 388–392 (2012).CAS 
Article 

Google Scholar 
Goldberg, L., Lagomasino, D., Thomas, N. & Fatoyinbo, T. Global declines in human-driven mangrove loss. Glob Chang Biol 26, 5844–5855, https://doi.org/10.1111/gcb.15275 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Brown, A. C. & McLachlan, A. Sandy shore ecosystems and the threats facing them: some predictions for the year 2025. Environ. Conserv. 29, 62–77, https://doi.org/10.1017/s037689290200005x (2002).Article 

Google Scholar 
Krumhansl, K. A. et al. Global patterns of kelp forest change over the past half-century. Proc. Natl. Acad. Sci. USA 113, 13785–13790, https://doi.org/10.1073/pnas.1606102113 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hill, N. K., Woodworth, B. K., Phinn, S. R., Murray, N. J. & Fuller, R. A. Global protected-area coverage and human pressure on tidal flats. Conserv Biol, https://doi.org/10.1111/cobi.13638 (2021).Murray, N. J. et al. Myanmar’s terrestrial ecosystems: Status, threats and conservation opportunities. Biol. Conserv. 252, 108834, https://doi.org/10.1016/j.biocon.2020.108834 (2020).Article 

Google Scholar 
Jackson, M. V. et al. Dual threat of tidal flat loss and invasive Spartina alterniflora endanger important shorebird habitat in coastal mainland China. J Environ Manage 278, 111549, https://doi.org/10.1016/j.jenvman.2020.111549 (2021).Article 
PubMed 

Google Scholar 
Davidson, N. C. & Finlayson, C. M. Updating global coastal wetland areas presented in Davidson and Finlayson (2018). Marine and Freshwater Research 70, 1195–1200, https://doi.org/10.1071/MF19010 (2019).Article 

Google Scholar 
Duan, H. et al. Identifying new sites of significance to waterbirds conservation and their habitat modification in the Yellow and Bohai Seas in China. Global Ecology and Conservation, e01031 (2020).Jung, M. et al. A global map of terrestrial habitat types. Scientific Data 7, 256, https://doi.org/10.1038/s41597-020-00599-8 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Keith, D. et al. The IUCN Global Ecosystem Typology v2.0: Descriptive profiles for Biomes and Ecosystem Functional Groups. (The International Union for the Conservation of Nature (IUCN), Gland, 2020).Fink, D. et al. Modeling avian full annual cycle distribution and population trends with citizen science data. Ecol. Appl. 30, e02056, https://doi.org/10.1002/eap.2056 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Convention on Biological Diversity. Indicators for the post-2020 Global Biodiversity Framework. (Convention on Biological Diversity, 2021).Murray, NJ. et al. High-resolution global maps of tidal flat ecosystems from 1984 to 2019, Figshare, https://doi.org/10.6084/m9.figshare.c.5884598.v1 (2022).Amante, C. & Eakins, B. W. ETOPO1 1 arc-minute global relief model: procedures, data sources and analysis. (US Department of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, National Geophysical Data Center, Marine Geology and Geophysics Division, 2009).Farr, T. G. et al. The shuttle radar topography mission. Rev. Geophys. 45, Rg200410.1029/2005rg000183 (2007).Article 

Google Scholar 
Mcowen, C. et al. A global map of saltmarshes. Biodiversity Data Journal 5, https://doi.org/10.3897/BDJ.5.e11764 (2017).Giri, C. et al. Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography 20, 154–159, https://doi.org/10.1111/j.1466-8238.2010.00584.x (2011).Article 

Google Scholar 
US Geological Survey. Product Guide: Landsat 4–7 Surface Reflectance (LEDAPS) Product (2018).US Geological Survey. Product Guide: Landsat 8 Surface Reflectance Code (LASRC) Product (2018).Foga, S. et al. Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sens. Environ. 194, 379–390 (2017).Article 

Google Scholar 
Breiman, L. Random forests. Machine learning 45, 5–32 (2001).Article 

Google Scholar 
Murray, N. J. et al. Code and data supplement to “High-resolution global maps of tidal flat ecosystems from 1984 to 2019”. Zenodo https://doi.org/10.5281/zenodo.6332960 (2020).Congalton, R. G. & Green, K. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. (CRC press, 2008).Lyons, M. B., Keith, D. A., Phinn, S. R., Mason, T. J. & Elith, J. A comparison of resampling methods for remote sensing classification and accuracy assessment. Remote Sens. Environ. 208, 145–153, https://doi.org/10.1016/j.rse.2018.02.026 (2018).Article 

Google Scholar 
Sagar, S., Roberts, D., Bala, B. & Lymburner, L. Extracting the intertidal extent and topography of the Australian coastline from a 28 year time series of Landsat observations. Remote Sens. Environ. 195, 153–169, https://doi.org/10.1016/j.rse.2017.04.009 (2017).Article 

Google Scholar 
Lee, J. et al. The first national scale evaluation of organic carbon stocks and sequestration rates of coastal sediments along the West Sea, South Sea, and East Sea of South Korea. Sci Total Environ 793, 148568, https://doi.org/10.1016/j.scitotenv.2021.148568 (2021).CAS 
Article 
PubMed 

Google Scholar 
Zhang, Z., Xu, N., Li, Y. & Li, Y. Sub-continental-scale mapping of tidal wetland composition for East Asia: A novel algorithm integrating satellite tide-level and phenological features. Remote Sens. Environ. 269, 112799, https://doi.org/10.1016/j.rse.2021.112799 (2022).Article 

Google Scholar 
Hooijer, A. & Vernimmen, R. Global LiDAR land elevation data reveal greatest sea-level rise vulnerability in the tropics. Nat. Commun. 12, 1–7 (2021).Article 

Google Scholar 
Rodríguez, J. P. et al. A practical guide to the application of the IUCN Red List of Ecosystems criteria. Philos. Trans. R. Soc. B-Biol. Sci. 370, 20140003, https://doi.org/10.1098/rstb.2014.0003 (2015).Article 

Google Scholar 
Keith, D. A. et al. The IUCN Red List of Ecosystems: Motivations, Challenges, and Applications. Conservation Letters 8, 214–226, https://doi.org/10.1111/conl.12167 (2015).Article 

Google Scholar 
Spencer, T. et al. Global coastal wetland change under sea-level rise and related stresses: The DIVA Wetland Change Model. Global and Planetary Change 139, 15–30 (2016).Article 

Google Scholar 
Bunting, P., Rosenqvist, A., Hilarides, L., Lucas, R. M. & Thomas, N. Global Mangrove Watch: Updated 2010 Mangrove Forest Extent (v2.5). Remote Sensing 14, 1034 (2022).Article 

Google Scholar 
US Geological Survey. Landsat 4–7 Collection 1 (C1) Surface Reflectance (LEDAPS) Product Guide. Version 3.0. (USGS, 2020).Xu, C. & Liu, W. Mapping and analyzing the annual dynamics of tidal flats in the conterminous United States from 1984 to 2020 using Google Earth Engine. Environmental Advances 7, 100147, https://doi.org/10.1016/j.envadv.2021.100147 (2022).Article 

Google Scholar 
Wang, X. X. et al. Rebound in China’s coastal wetlands following conservation and restoration. Nature Sustainability 4, 1076-+, https://doi.org/10.1038/s41893-021-00793-5 (2021).Article 

Google Scholar 
Fitton, J. M., Rennie, A. F., Hansom, J. D. & Muir, F. M. E. Remotely sensed mapping of the intertidal zone: a Sentinel-2 and Google Earth Engine methodology. Remote Sensing Applications: Society and Environment, 100499, https://doi.org/10.1016/j.rsase.2021.100499 (2021).Murray, N. J., Kennedy, E., Álvarez-Romero, J. G. & Lyons, M. B. Data freshness in ecology and conservation. Trends in Ecology and Evolution 36, 485–487, https://doi.org/10.1016/j.tree.2021.03.005 (2021).Article 
PubMed 

Google Scholar  More