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Mapping kelp persistenceThe study area for this analysis encompasses the region where Macrocystis pyrifera is the dominant canopy kelp species in the Northeast Pacific Ocean. The region extends from Año Nuevo Island in the north (latitude ~37.1°), California, USA, to Punta Prieta in the south (latitude ~27°), Baja California Sur, Mexico. We mapped the distribution of giant kelp canopy and characterized persistence using a 30-m resolution satellite-based time series covering our entire study area27. These data provide quarterly estimates of kelp canopy area across the study region from 1984 to 2018. We estimated giant kelp canopy from three Landsat sensors: Landsat 5 Thematic Mapper (1984–2011), Landsat 7 Enhanced Thematic Mapper+ (1999–present), and Landsat 8 Operational Land Imager (2013–present). We downloaded all imagery as atmospherically corrected Landsat Collection 1 Level-2 products. Each Landsat sensor has a pixel resolution of 30 × 30 m and a repeat time of 16 days (8 days when two Landsat sensors were operational). Since Landsat imagery can be obscured by cloud cover, we obtained a clear estimate of kelp areas ~16 times per year from 1984 to 2018 (mean = 16.2, std = 4.1). The repeated observations across the time series avoid missing kelp canopy due to physical processes such as tides and currents. Multiple Landsat passes over seasonal timescales are successful at mitigating the effect of tide and tidal currents on Landsat kelp canopy detection27.While the pixel resolution of Landsat sensors is 30 × 30 m, we were able to observe the presence and density of kelp canopy on subpixel scales using a fully automation procedure. We first masked all land areas using a global 30 m resolution digital elevation model (asterweb.jpl.nasa. gov/gdem.asp) and classified the remaining pixels as seawater, cloud, or kelp canopy using a binary decision tree classifier trained on a diverse array of pixels within the study region27. We then used Multiple Endmember Spectral Mixture Analysis39 to model each pixel as the linear combination of seawater and kelp canopy. This method can accurately obtain kelp canopy presence as long as kelp canopy covers ~13% of a 30 m pixel. These methods were validated using 15 years of monthly kelp canopy surveys by the Santa Barbara Coastal Long Term Ecological Research project at two sites in Southern California. We filtered errors of commission (such as free-floating kelp paddies) by removing any pixels classified as kelp canopy in More

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ParticipantsThe sample consisted of 36 participants (Mage = 22.72, SDage = 1.91), which were recruited during the autumn semester at the Saint-Charles cafeteria of the University Paul Valery, France. All participants completed an informed consent form and reported being right-handed, had normal or corrected-to-normal visual acuity, and normal motricity. We chose a sample composed exclusively of women due to the existence of differences between men and women in the use of SR26. The aim was also to avoid a possible effect of sexism (e.g., men might not use the SR because it is a woman)27,28. The sample size was determined with G*Power29. A hypothesized effect size of − 0.43 was chosen, based on a previous study23; a corresponding power analysis (effect size r = − 0.43, α = 0.05, power = 0.80) resulted in an estimation of 32 participants. The final sample size (36) was chosen to meet methodological needs. The study was conducted according to the Declaration of Helsinki and were reviewed and approved by the Scientific and Ethics Committee of Epsylon Laboratory EA4556, University Paul Valéry of Montpellier.Materials and stimuliA schematic representation of the experimental design is presented in Fig. 1 left-hand side. Three, rectangular tables were placed some distance apart from each other. In the real action task, toilet tissue rolls (either 16 or 8) were positioned in rows on Table A, with each row containing four rolls (e.g., for eight rolls, there were two rows of four rolls). The aim was to use objects that can be grasped with hands and light enough to limit the impact of the weight. Participants were asked to move the rolls from Table A to Table B and to put them into containers disposed on Table B. There were two containers (height: 30 cm, width: 55 cm, depth: 35 cm) in order to avoid interference between the participant and the SR during the real action task. A beeper was placed on Table C. Participants had to press on this beeper to ask for help from the SR. For all participants, the SR was a 23-year-old woman student who sat on a chair 2 m from Table A. The distances AB and AC were 2 m and 3.5 m, respectively.Figure 1Schematic representation of the design used in Experiments 1 and 2.Full size imageThe decision task (Fig. 2) was computer-based, and developed with OpenSesame software30. Participants were seated at a table located 3 m from Table A, in front of a monitor and keyboard (screen size: 14-inch; distance participant-screen: 75 cm; distance participant-keyboard: 30 cm). Photographs showing a quantity of 4 (Q4), 8 (Q8), 12 (Q12), 16 (Q16), 20 (Q20), and 24 (Q24) rolls were displayed on the screen.Figure 2Experimental design for decision task in Experiment 1 and 2.Full size imagePersonality traits were assessed by a self-report questionnaire: the Big Five Inventory (BFI)31 translated and validated in a French population32. This questionnaire is based on the “Big Five”, the five dimensions consensual model of adult personality33. The Big Five dimensions are: Conscientiousness, Agreeableness, Neuroticism, Openness to experience, and Extraversion.ProcedureParticipants were informed that the experiment was composed of four phases. In the first phase they would be asked to fill out the BFI. In a second phase called the real action task, they would have to move different quantities of rolls with their hands, or with the help of a SR. In the third phase, the decision task, they could decide to use the SR or not depending on the actions performed in the second phase. Finally, they were told that in the last, fourth, phase they would be asked to move different quantities of rolls depending on the choices they had made in the decision task (third phase). Here, the aim was to raise the participant’s awareness of the impact and importance of the decision task, but this fourth phase was never actually done.After the BFI completion, participants began phase two. Here, they were asked to physically move rolls from Table A at normal speed (i.e., without any time constraint) and place them in the container on Table B. This scenario was designed to reflect a real action task of storing purchases after returning from the supermarket. The experimenter asked participants to perform four trials: two in the Hands condition (quantities 8 and 16), and two in the SR condition (quantities 8 and 16). In the Hands condition, participants had to move all the rolls from Table A to Table B and only two at a time. Once the task was completed, they had to return to Table A. In the SR condition, participants had to move all rolls with the help of the SR. Here, they had to move from Table A, go to Table C to press the beeper (which sounded instantly), then return to Table A and move rolls with their hands, two at a time, to the container on Table B. Simultaneously, the co-actor got up from her chair and went to Table A to help participants to move the rolls. The co-actor also moved two rolls at a time, in synchrony with participants. Thus, participants and the co-actor moved four rolls, at the same time, with their hands. When the task was completed, participants had to go back to Table A, then Table C, press the beeper a second time, and finally return to Table A. The variables Quantity of rolls (8 vs. 16) and Condition (Hands vs. SR) were counterbalanced between participants, leading to the four following orders: (1) Hands/Q8, SR/Q16, Hands/Q16, SR/Q8, (2) Hands/Q16, SR/Q8, Hands/Q8, SR/Q16, (3), SR/Q8, Hands/Q16, SR/Q16, Hands/Q8, (4) SR/Q16, Hands/Q8, SR/Q8, Hands/Q16.As mentioned above, decision task (third phase) was computer based. Each trial began with a white fixation cue presented in the center of a black screen displayed for 1500 ms. The fixation cue was immediately followed by a photograph showing a quantity of rolls (Q4 vs. Q8 vs. Q12 vs. Q16 vs. Q20 vs. Q24) displayed for 3000 ms on the screen. Participants had to evaluate 20 times each Quantity of Rolls (6 Quantity of Rolls × 20 Blocks), making a total of 120 photographs to be evaluated. They were asked to respond as rapidly as they could before the photograph disappeared from the screen. In case they did not respond to a trial in the allotted time, the trial was presented later in its corresponding block. Participants were asked to press the ‘a’ key with their left index finger, or the ‘p’ key with their right index finger if they considered being faster to move the rolls with the SR or by hands. The response assigned to each key was counterbalanced across participants and the quantity of rolls was fully randomized. Response times and decisions were collected for each of the 120 photographs. Participants were informed that after the decision task, the software would randomly choose 12 photographs, and in a second real action task (i.e., fourth phase), they would have to move these quantities of rolls depending on the choices they had previously made. Moreover, they were informed that the SR was at their complete disposal and they were invited to choose its use according to their own estimate. In fact, once the decision task was over, they were informed that they would not have to complete the fourth phase. Upon recruitment, they were told that the experiment would take 40 min (i.e., enough time to also complete the fourth phase). However, the actual duration of the experiment was 30 min (the time to complete the BFI, the real action task and the decision task).ResultsRaw responses were converted to a binary response, based on the participant’s choice (0 for Hands, 1 for SR). These data were then fitted locally using the ModelFree software package34, giving a point of subjective equality (PSE-SR) for each participant. Specifically, the PSE-SR indicates the quantity of rolls for which the participant considered the use of their hands, or the SR as equivalent. After a visual inspection of the psychometric curve, data of four participants were excluded from the analysis because these participants had particularly flat curve (i.e., slope = 0). Then, we used the Shapiro test to test the normality of distributions of all dependent variables. PSE-SR, Neuroticism, Openness to experience, Conscientiousness, and Extraversion were identified to be normally distributed (all W [0.940, 0.979], and p [0.070, 0.78]), while Agreeableness was not (W = 0.933, p = 0.048). Hence, the use of parametric statistical tests was proscribed, and the analysis of the correlation matrix was performed with the Spearman non-parametric method.Correlations matrix between the five personality factors, and the PSE-SR score are given in Table 1, which highlights a correlation between Extraversion and PSE-SR (Fig. 3). Data were also examined by estimating Bayes factors comparing fit under the null hypothesis (PSE-SR is not a function of personality trait), and the alternative hypothesis (PSE-SR is a function of personality trait). Bayes factors were, respectively, BF01 = 3.481 for Neuroticism, and BF01 = 3.728 for Conscientiousness. These results confirm the null hypothesis, as Bayes factors are above three35. Bayes factors were, respectively, BF01 = 1.550 for Openness to experience, and BF01 = 1.117 for Agreeableness. As these results do not support either the null nor the alternative hypothesis, a multiple regression was conducted to test whether Extraversion, Agreeableness, and Openness to experience predicted PSE-SR.Table 1 Spearman correlation coefficients for PSE-SR and personality factors, and among personality factors for Experiment 1.Full size tableFigure 3Correlation between Extraversion and PSE-SR found in Experiment 1.Full size imageThe multiple regression showed that these three factors explain a significant amount of variance in the value of PSE-SR, F(3,31) = 5.087, p = 0.006, R2Adjusted = 0.283. Specifically, Agreeableness (β =  − 0.141, t =  − 0.882, p = 0.385, 95% CI = [− 0.258, 0.103]), and Openness to experience (β =  − 0.159, t =  − 1.023, p = 0.314, 95% CI = [− 0.237, 0.079]) did not significantly predict the value of PSE-SR. However, Extraversion did (β =  − 0.480, t =  − 2.958, p = 0.006, 95% CI = [− 0.402, − 0.073]).DiscussionThe key finding of Experiment 1 is the significant negative linear relationship between Extraversion and PSE-SR. This result supports our hypothesis, and shows that participants’ Extraversion level predicts the use of the SR. The more participants are extraverted, the more they perceive the SR as a benefit. This is consistent with the work of Amirkhan et al.23, which shows that extraverts seek help much more quickly than introverts. 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Our analysis suggests that, over 465 ha of mangrove area, almost 83% of aboveground tree biomass were harvested annually for commercial timber purposes, using a keyhole harvest pattern (Fig. S3b). Yet after 25 years of natural and human-induced regeneration, both field- and satellite-based assessments reveal that biomass carbon stocks and canopy cover had fully recovered. Our approach using space-for-time substitution indicates that manual selective logging did not significantly affect soil carbon stocks and rates of annual carbon burial. While the differences in soil carbon stock between sites may be due to the diverse hydro-geomorphic settings8,14 the mangrove root mass in the top 1-m were not disturbed by manual logging activities. Similar situation was found in Tampa Bay, Florida where peat formation from root mass has enhance carbon sequestration15. These findings reduce uncertainty around the effects of mangrove forest management on the long-term functional capacity of blue carbon storage and provide evidence that managed mangrove ecosystems may deliver nature-based climate solutions.Recovery of forest structure, canopy cover and species diversityAlong carbon stocks, forest structure and species diversity also demonstrated recovery (Fig. 2, Table S1). Seedling densities were significantly higher in 5 year-old mangrove plots than in plots at any other stage (F(5,13) = 28.321, p  More

Genetic diversity of E. chinense based on COIThe partial fragment of COI, 595 bp in length, was sequenced from 113 E. chinense individuals collected from the eleven sites in South Korea (Table 1). The resultant COI sequences were aligned together with 27 COI individual sequences12,13,14 retrieved from the NCBI GenBank (Table 1). The latter consists of 26 from six collection sites in South Korea and one from a Japanese site. Hence, 140 COI sequences of E. chinense were analyzed, representing 18 collection sites at the nine populations in South Korea and Japan (Table 1). Based on the alignment set (no indels) of these 140 COI sequences (Data S1), we obtained a total of 58 COI haplotypes, of which 43 were singleton, appeared in only a single site. The novel 41 out of 58 COI haplotypes obtained were registered under the GenBank accession nos. MW265437–MW265477 (Table S2). According to the sequence alignment of the 58 COI haplotypes (Fig. S2; Data S2), there were 71 polymorphic sites and 31 parsimoniously informative sites (Fig. 1C), among which four adenine/guanine hotspots at 207, 282, 354, and 420 were ascertained to articulately divide the haplotypes of E. chinense into four meaningful phylogenetic groups: (a) A(207)–A(282)–A(354)–A(420), (b) A–A–G–A, (c) G–A–G–A, and (d) G–G–G–G.Table 1 List of collection sites and the number of individuals of Ellobium chinense with genetic markers applied to each of the nine populations in South Korea and Japan.Full size tableBased on the COI haplotype sequence alignment (Fig. S2; Data S2), we reconstructed a ML tree using Ellobium aurisjudae as an outgroup. In the resultant tree topology (Fig. S3), it was confirmed that E. chinense appeared as a monophyletic group, but no distinction between the haplotypes from each geographical population was observed. To define detailed relationships among the COI haplotypes, the outgroup was removed and then an unrooted ML tree (Fig. 1D) was reconstructed. The resultant tree showed two distinctive phylogenetic groups, namely A–A–A–A and the other groups (including at least one G or more in the four positions), regardless of collection localities. The A–A–A–A group included 35 of the 58 COI haplotypes. The others could be divided into the A–A–G–A group (N = 12: ECH11, 12, 15, 16, 18, 23, 27, 28, 32, 33, 36, and 49), the G–A–G–A group (N = 1: ECH35), and the G–G–G–G group (N = 8: ECH01, 07, 19, 29, 41, 45, 48, and 54).As shown in Table S2 and Fig. S3, ECH01 was a dominant member of the G–G–G–G group with the most individuals (27), which appeared across all the South Korean populations examined here. As shown in Fig. 1D, the A–A–A–A group is likely to be an ancestral type because it was most frequently found in the other species within Ellobiidae (unpublished data) and its haplotype diversity was the highest among the four genetic groups. Given that the G–G–G–G group exhibited much lower haplotype diversity than the A–A–A–A group, and was not observed in any other ellobiid species (unpublished data), it is reasonable to suggest that the G–G–G–G group is a derived rather than an ancestral type. Thus, as shown in Fig. 1D, it is conceivable that unidirectional and stepwise A → G transition events from A–A–A–A to G–G–G–G may have been occurred in E. chinense. Within the A–A–A–A and A–A–G–A groups, parsimoniously informative A → G transition events were found at the sites 120 (ECH12, 15, 16, 18, 23, 38, 40, 44, and 49) and 183 (ECH3, 9, 10, 20, 43, 50, and 55), with a few exceptional cases of G → A at the sites 216 (ECH12, 15, 16, 18, and 23), 372 (ECH12, 15, and 23), and 429 (ECH37, 38, 46, and 47; ECH19 found in the G–G–G–G group).As indicated in Table 2, the nucleotide diversity (π) is relatively low among the nine populations of E. chinense, ranging from 0.00749 (population BG) to 0.01042 (SC) with an average of 0.00865, whereas the haplotype diversity was very high across these populations, ranging from 0.924 (YG) to 1.000 (SC and JB) with an average of 0.939). All values of Tajima’s D and Fu’s FS were congruently negative, with averages of − 1.87100 (P  More