Philippa Kaur

Historical changes and current status of the Dongzhaigang mangrove areaBased on the literature and remote sensing data, we calculated the changes in the area of mangrove forests in Dongzhaigang since the 1950s presented in Fig. 2. In the last 60 years, the area of mangrove forests in Dongzhaigang has experienced large fluctuations mainly due to human destruction and protection activities such as mariculture reclamation, cofferdams, and restoration: it decreased from 3416 hm2 in 195617 to 3213 hm2 in 195919,29 and then decreased sharply to 1733 hm2 in 1983 and to 1537 hm2 in 198720,30. Since the establishment of the national nature reserve in 1986, the decline in area of Dongzhaigang mangrove has stopped19, which are now protected and restored owing to the law and regulations that prohibit human activities from destroying the mangrove resource. In 1988, the area was restored to 1809 hm2, and since the 1990s, it has no longer decreased, remaining constant at approximately 1711 hm2 (in the range of 1575–1812 hm2) based on the literature)18,20,31,32,33,34 (Fig. 2). The area of the Dongzhaigang mangrove forest in 2019 was estimated to be 1842 hm2 based on the latest 2 m resolution remote sensing data21. Hence, we wonder how SLR has impacted Dongzhaigang mangrove in the past decades. However, it is very difficult to analyze how SLR has historically impacted the spatial changes in the Dongzhaigang mangrove; the same can be said regarding the influence of human activities, such as destruction before mid-1980s and protection after 1990s. However, the dynamic changes among low plant edges in the intertidal zone can be used to analyze the impact of natural driving forces such as SLR35, based on the latest remote sensing data for the period of 1986–2020. Thus, we analyzed the dynamic changes in low mangrove edges (hereafter, the edges), which are mainly impacted by natural impact drivers, as shown in Fig. 3. The dynamic low mangrove edges represented by 1986, 2000, and 2020 reveal the changes in spatial distribution of Dongzhaigang mangrove. As shown in Fig. 3. Most of the edges along the coast of Dongzhaigang between 1986 and 2020 migrated landward, but not significantly. However, if we look at the changes in detail, some edges such as those in Daoxue, Sanjiang (purple circles in Figs. 3a,b–d,e–g) more clearly retreated landward compared to other places. Besides, some edges of Luodou along the northeastern coast of Dongzhaigang outside the reserve and an unnamed small island (pruple circles in Fig. 3a,h–j) also migrated landward very distinctly. On the contrary, the two smaller shore lines (black circles) in the northern part of Yangfeng and Daxue districts showed seaward expansion (Fig. 3a).Figure 2Changes in the mangrove area in Dongzhaigang from 1956 to 2019. The equation in the upper-right-hand corner of the plot refers to the fitting equation of historical changes in the total area of Dongzhaigang mangrove.Full size imageFigure 3The dynamic changes in low mangrove edges in Dongzhaigang from 1986 to 2020. Maps generated in ArcMap v10.0 (https://www.esri.com/en-us/home).Full size imageVertical rate of sediment accretion in mangrove wetlandsThe vertical rate of sediment accumulation in mangrove wetlands can reflect whether the mangroves can adjust the soil surface elevation change through sediment trapping to adapt to SLR6,11. The vertical sediment accretion rates at two sites of Dongzhaigang mangrove (i.e., Linshi and Daoxue villages in Fig. 1b) can be obtained from historical documents, which are 0.41 cm year−1 at LS and 0.64 cm year−1 at DX, respectively27,28. Since historical data may not be enough to reflect the vertical sediment accretion rates in time and space, we conducted a supplementary investigation on the sediment accumulation rates at site HG in Yanfeng and SJ site in Sanjiang farms, respectively (Fig. 1b), based on the assumption that they can reflect the sediment supplies from main reivers such as Yanfeng West River and Yanzhou River, respectively. Sediment accretion rates measured using 210Pbex specific activity in the cores from sites HG and SJ showed that 210Pbex decayed exponentially with increasing depth, and the R2 values of both cores were approximately 0.80 after curve fitting. This analysis resulted in vertical sediment accretion rates of 0.53 and 0.40 cm year−1 at HG and SJ, respectively (Fig. 4). Therefore, the locations of sediment cores at sites LS, DX, HG, and SJ can basically represent the whole Dongzhaigang mangrove forest area.Figure 4210Pbex activity profiles in selected cores such as from (a) station HG and (b) station SJ.Full size imageRate of relative sea level rise in Dongzhaigang mangroveThe global mean sea level (GMSL) is accelerating due to global warming-induced thermal expansion of the oceans and melting of land-based glaciers and ice caps into the sea36. Between 1901 and 2010, the GMSL rose by 0.19 m9. Coastal China is among the regions that experience the highest levels of SLR23. The rate of RSLR along China’s coast from 1980 to 2019 was 3.4 mm year−1, higher than the global average23. In the future, under the premise of increasing anthropogenic GHG emissions, global sea levels will rise rapidly, and it is projected that the GMSL may rise by 0.84 m (0.61–1.10 m) relative to the current levels by the end of the twenty-first century9. Based on the observations from the tide gauge stations in the Haikou area and model data from the Coupled Model Intercomparison Projection 5 (CMIP5), the rate of RSLR around Dongzhaigang reached 4.6 mm year−1 from 1980 to 2018. This rate is much higher than the global and China’s average values23,25 and will likely accelerate further in the future. Based on the results of the CMIP5 model simulations under different GHG emission scenarios24, the RSLR in coastal Haikou waters, including in Dongzhaigang, is expected to be significant by 2030, 2050, and 2100 for the low, intermediate, and very high GHG emission scenarios RCPs 2.6, 4.5, and 8.5, respectively (Table 1, Fig. 5). Under RCPs 2.6, 4.5, and 8.5, the sea level will rise by 65 (42–90, likely range), 75 (51–102, likely range), and 96 (70–125, likely range) cm by 2100, respectively, with the average RSLR rates of 6.84 (4.42–9.47, likely range), 7.89 (5.37–10.74, likely range), and 10.1 (7.37–13.12, likely range) mm year−1, respectively.Table 1 Estimated coastal relative sea level rise (cm) and its rate (mm year−1) in the Haikou area under different GHG emission scenarios (data from Kopp et al.24).Full size tableFigure 5Historical and future relative sea level changes along coastal Dongzhaigang, Haikou City from 1980 to 2100; the 5–95% uncertainty ranges are shaded for RCPs 2.6, 4.5, and 8.5, respectively.Full size imageImpact of relative sea level rise on Dongzhaigang mangroveMangroves cannot easily adapt to rising sea levels if the rate of GMSL rise exceeds 6.1 mm year−1 ( > 90% probability, very likely), whereas the survival threshold for mangroves is extremely likely to be exceeded ( > 95% probability, extremely likely) when the rate of GMSL exceeds 7.6 mm year−17. Although these values are based on global levels7, they still reflect the threat of SLR to local mangroves. In view of this, we further analyzed the potential impact and risks to Dongzhaigang mangrove from future SLR under different climate scenarios.Based on the predicted future rates of SLR under RCPs 2.6, 4.5, and 8.5 and on the vertical sediment accretion rates of Dongzhaigang mangrove wetlands, the mangroves are likely to be affected by rising sea levels by 2030, 2050, and 2100, respectively (Table 2, Fig. 6). Under the low GHG emission scenario (RCP 2.6), the area of the Dongzhaigang mangrove forest will only experience a small reduction: 16.40% (1.20–16.95%, likely range), 302 hm2 (22–312 hm2, likely range); 16.73% (1.20–17.82%, likely range), 308 hm2 (22–328 hm2, likely range); and 17.60% (1.14–31.02%, likely range), 324 hm2 (21–571 hm2, likely range) by 2030, 2050, and 2100, respectively (Table 2, Fig. 6a). This is because the vertical sediment accretion rate of Dongzhaigang mangrove will remain largely constant with increasing RSLR rate. Moreover, it should be noted that compared with 2030, the increase areas of mangroves inundation caused by SLR will be small by 2050 under three RCPs scenarios (Table 2). In contrast, under the intermediate and very high GHG emission scenarios (RCPs 4.5 and 8.5), Dongzhaigang mangrove is expected to be more significantly affected by SLR. Under RCP 4.5, 26.56% (16.19–40.74%, likely range) or 489 hm2 (298–750 hm2, likely range) of mangrove forest will likely be lost by the end of the century (Table 3, Fig. 6b). Under RCP 8.5, it is projected that 31.99% (18.14–50.73%, likely range) or 589 hm2 (334–934 hm2, likely range) of mangrove forest will be lost by 2100 (Table 2, Fig. 6c). Therefore, under RCPs 4.5 and 8.5, the impact of SLR on mangrove wetlands by 2100 is much higher than that of RCP 2.6, and is likely to result in  > 26% of mangroves being lost, whereas under RCP 2.6, only 17% of mangroves are likely to be lost.Table 2 Area (hm2) and percentage of future mangrove loss in Dongzhaigang under different climate scenarios (RCPs 2.6, 4.5, and 8.5) (likely ranges).Full size tableFigure 6Potential loss of mangrove forests in Dongzhaigang under different climate scenarios (RCPs 2.6, 4.5, and 8.5). Maps generated in ArcMap v10.0 (https://www.esri.com/en-us/home).Full size imageTable 3 Core stations and depths.Full size tableUnder RCP 2.6, the rate of RSLR around Dongzhaigang will reach 0.72 cm year−1 in 2030 and then decrease in 2050 and 2080 to 0.69 and 0.68 cm year−1, respectively (Table 1). However, under RCP 4.5 (8.5), by 2030, 2050, and 2100, the rate of RSLR will reach 0.72 (0.72), 0.73 (0.80), and 0.79 (10.1) cm year−1, respectively. By 2100, some mangroves in the northern part of Tashi village, the eastern part of Yanfeng, the northern part of Daoxue Village, and the northeastern part of the Sanjiang farm will likely be lost owing to SLR, and other coastal wetlands will also be impacted. Since the rate of RSLR around Dongzhaigang is higher than the global average survival threshold for mangroves (i.e., the SLR rate exceeds 7.0 mm year−1), the Dongzhaigang mangrove will be significantly affected by SLR, with a potential loss of 31–32%; however, the survival threshold will not increase (Table 2, Fig. 6). More

Coupled model experiments for detecting vegetation-climate feedbackWe quantified changes of near-surface (2-m) air temperature (Ta) in response to the observed NH greening for all active growing seasons during 1982–2014 using IPSL-CM. We defined the three growing seasons (spring, summer, and autumn) across the entire NH domain as periods of March-April-May (MAM), June-July-August (JJA), and September-October-November (SON), respectively. For each season, a pair of transient numerical experiments was performed by modifying LAI: a dynamic vegetation experiment (SCE) forced by annually and seasonally varying LAI from satellite observations36, and three seasonal control experiments (({{{{{{rm{LAI}}}}}}}_{{{{{{rm{CTL}}}}}}}^{{{{{{rm{MAM}}}}}}}), ({{{{{{rm{LAI}}}}}}}_{{{{{{rm{CTL}}}}}}}^{{{{{{rm{JJA}}}}}}}), and ({{{{{{rm{LAI}}}}}}}_{{{{{{rm{CTL}}}}}}}^{{{{{{rm{SON}}}}}}}) for MAM, JJA, and SON, respectively) forced by annually varying LAI for all seasons, except in the season of interest when the LAI was fixed to the climatological conditions observed during 1982–2014 (Fig. S1). For all experiments, other boundary conditions, including sea surface temperature (SST), sea ice fraction (SIC), and atmospheric CO2 concentrations, were kept consistent (Methods). Therefore, differences between SCE and the control experiments characterized the effects of the observed LAI changes on Ta (hereafter denoted as ΔTa), both intra- and inter-seasonally. Multimember paired ensembles were generated for each coupled model experiment by performing 30 repeated runs but with different initial conditions (see Methods).The capacity of the IPSL-CM GCM for simulating the seasonal variations and spatial patterns of Ta was assessed by comparing the SCE simulation results with the observation-based Ta data (Methods). Throughout most of the growing season (May to October), the SCE simulation well reproduced the increasing trend and interannual variability of the NH land mean Ta observed during 1982–2014 (Fig. S2). Observational data showed that the strongest NH warming occurred in early spring (March and April) and late autumn (November). However, the SCE simulation failed to capture the exceptionally strong warming during the transitional seasons, leading to the underestimation of the annual mean warming trend (SCE: 0.237 ± 0.024 °C decade−1; observed: 0.362 ± 0.048 °C decade−1). This underestimation stemmed from a negative bias in the increase of downwelling shortwave radiation, possibly due to an absence of short-lived forcing and bias in the cloud systems37. Overall, the SCE reproduced the geographical patterns of seasonal warming reasonably well (Fig. S3), which strengthened our confidence in the model projections. Notably, it successfully captured the observed amplified warming over pan-arctic and semi-arid regions, as well as the few cases of regional cooling, such as that over northwestern North America during MAM (Fig. S3).Intra-seasonal temperature responses to NH LAI changesFor the period from 1982 to 2014, satellite-retrieved LAI showed statistically significant increasing trends (p  0.1), strong and significant JJA cooling (−0.044 ± 0.008 °C decade−1, p  0.1) (intra-seasonal feedbacks shown in Fig. 1b). The LAI-induced JJA Ta trend was equivalent to cooling of −0.15 ± 0.03 °C in JJA over the study period, offsetting the overall SCE-simulated near-surface air warming over this period by ~12.5%. This strong JJA cooling was further supported by a significant negative correlation (r = −0.64, p  0.1) or SON (r = 0.07, p  > 0.1) (Fig. S4a, c), during which the LAI-induced changes accounted for only 1.3% (MAM) and −3.2% (SON) of the concurrent greenhouse warming. We also verified the robustness of our results by performing equilibrium experiments with an independent model, the NCAR Community Atmosphere Model coupled with Community Land Model (CAM-CLM, Methods). Indeed, this model generated a similarly strong LAI-induced cooling in JJA (−0.18 °C, p  0.1) and SON (−0.05 °C, p  > 0.1) (Fig. S5).Fig. 1: Intra- and inter-seasonal temperature responses to leaf area index (LAI) changes.a Monthly trends (shadings) of Northern Hemisphere (NH) mean LAI during 1982–2014 used as input to the seasonal simulations. The dashed curve and transparent bars indicate trends of monthly LAI and seasonally aggregated LAI values, respectively. b Linear trends of Ta driven by LAI changes within the same season (intra-seasonal) and other growing seasons (inter-seasonal). Error bars in a, b indicate uncertainty ranges [1 – standard deviation (SD)]. c Monthly trends of LAI-induced air temperature changes (ΔTa), with red and blue shadings representing positive and negative trends, respectively. The bottom panel shows the overall ΔTa trends induced by LAI changes in all growing seasons, calculated as the sum of ΔTa trends from the three seasonal runs shown separately in the above panels. ***p  More

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Data collectionThe present dataset was collected within the framework of the Atlantic Lobster Moult and Quality (ALMQ) project originally managed and implemented by the Atlantic Veterinary College Lobster Science Centre at the University of Prince Edward Island in collaboration with the Fishermen and Scientists Research Society. The Atlantic Lobster Moult and Quality project was initially funded through the Atlantic Innovation Fund program from the Atlantic Canada Opportunities Agency (ACOA) and transferred to the Fishermen and Scientists Research Society (FSRS) in 2012.Sampling took place every 2–3 weeks in eight lobster fishing areas (LFA) in Atlantic Canada from 2004 to 2014 (see Fig. 1, Table 1). The sampling followed the FSRS Lobster Moult and Quality sampling protocol and was conducted by technicians from the Atlantic Veterinary College and the Fishermen and Scientists Research Society in fixed locations from traps set the day before2. Locations based on targeted sampling (LFA 33 and 34) were chosen according to the fishing efforts in the respective areas and selected by a lobster science committee consisting of members from industry, academia, research and federal and provincial representatives. Other locations (LFA 24, 25, 26A, 35) were chosen based on proximity to the Atlantic Veterinary College and other projects with commercial fishers which allowed sampling.Table 1 Overview of sampling locations, surface areas (km2) and number of lobsters (N) sampled for the Atlantic Lobster Moult and Quality Project by AVC Lobster Science Centre from 2004–2015 in Atlantic Canada. (PEI = Prince Edward Island, NS = Nova Scotia).Full size tableFig. 1(a) Map of the lobster fishing areas (LFAs) in the Maritime Provinces in eastern Canada with the sampling locations (red) recorded by the AVC Lobster Science Centre for the Atlantic Lobster Moult and Quality project. (b) Enlarged map of LFA 33. (c) Enlarged map of LFAs on Prince Edward Island. The maps were created using QGIS (v. 3.18; https://qgis.org). Contours depict water depths in meters.Full size imageFor each sampling event, 40 commercial lobster traps with escape vents for lobsters below the minimum legal size were used. Legal sizes depend on size-at-maturity (size at which 50% of the population reach maturity) which differs between LFAs due to regional differences in water temperature that influence lobster growth. There were some differences in sampling procedure between lobster fishing season and off-season. During lobster fishing season sampling took place within 48 h post landing and only legal-sized lobsters were assessed. During off season, lobsters were sampled directly on board chartered boats and were returned to sea immediately after sampling. During non-fishing season sampling, lobsters below minimum legal size were also sampled but no egg-bearing females were targeted to minimize negative handling effects. Targeted sample size was 200 lobsters per sampling event before 2009 and 125 lobsters after 2009 due to budget constraints.On average, 3–4 lobsters of each sex were sampled in every 2 mm lobster size grouping. Lobster size was recorded as the carapace length in mm and determined using calipers rounding down to the nearest mm. The size distribution of sampled lobsters is presented in Fig. 2. Lobsters were assessed for general health (lesions, shell damage, liveliness/vigour) and shell hardness. Shell hardness was recorded as soft, medium or hard. A carapace of a soft-shelled lobster would be compressible at the ventral and dorsal (anterior and posterior) carapace, a medium-shelled lobster would only be compressible at the ventral carapace and a hard-shelled lobster would not be compressible at any carapace location.Fig. 2Lobster size (as carapace length in mm) distribution for all lobsters sampled during the sampling period (15 missing values).Full size imageTo estimate hemolymph protein levels, the ventral abdomen between the first pair of walking legs was sprayed with 70% ethanol and 3 ml of hemolymph were extracted with a 22 gauge needle and a 3 ml syringe. A few drops of hemolymph were placed on a handheld refractometer and the refractive index (“°Brix” value) was recorded and used as a proxy for total hemolymph levels. The distribution of hemolymph protein level is shown in Fig. 3. The moult stages were determined by pleopod stages under a stereomicroscope and recorded in pleopod stages (see Table 2). The stage determinations are shown in Table 2 and Fig. 46.Fig. 3Distribution of hemolymph protein level (measured in °Brix) for all lobsters sampled in the dataset (892 missing values).Full size imageTable 2 Description of premoult stages and pleopod stages in adult American lobster based on Aiken6. C: Intermoult, D: Premoult.Full size tableFig. 4Pleopod stages of lobsters at different times in their moult cycle. Illustrations by Lavallée et al.2.Full size imageIn total, 141,659 lobsters were sampled from 2004–2015 over 1,195 sampling events. Data were recorded manually on data sheets and re-checked before being entered into an Excel data sheet (Excel, Microsoft). More

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