Ecology

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Historical land use and cover changesExisting databases differed significantly in representing historical LUCC in China (Fig. 1). Generally, datasets agree on the direction of change in cropland area until 1980 in Liu and Tian18, Ramankutty19, Houghton20, and this study (Fig. 1b, c), while the magnitude of change varied greatly. Specifically, the total cropland expansion in China was comparable between our new data set and the LUH2-GCB from 1900 onwards (56 vs 60 Mha, Fig. 1b), but cropland area changes since 1980 diverged considerably (−14 vs 41 Mha, Fig. 1c). The differences were also evident across space and more distinct during the period of 1980 to 2019 (Fig. 2a–d), in which the cropland coverage was mainly declining in our reconstructed data but increasing in LUH2-GCB (Fig. 2b, d). We found that the distinct changes are derived from the abrupt cropland increases in the FAO data reported from China, upon which LUH2-GCB was based (see Supplementary Information 3).Fig. 1: Temporal, net changes of cropland and forest from 1900 (unit: Mha).Panel a–c: cropland; panel d–f: forest; the bar charts indicate the total accumulated areas (b, e) from 1900 and (c, f) from 1980 until the last available year; LUH2-GCB was the latest version of LUH2 data used in Global Carbon Budget assessments projects (LUH2 used in MsTMIP and TRENDY were showed in Supplementary Figs. S7 and S10); Houghton data were derived from Houghton and Nassikas20 and the data in 1900 were interpolated from 1850 and 1950; Liu&Tian and Ramankutty data were derived from the works of Liu and Tian16 and Ramankutty and Foley18; the open circles indicate the changes of cropland and forest areas derived from inventory-based benchmark data; details of the benchmark data for cropland and forest were presented in Yu et al.11 and Supplementary Information 1.2 of this study, respectively; error bars: one standard deviation from the mean.Full size imageFig. 2: Spatial distribution of the fractional coverage changes of cropland and forest in China (unit: %).Panels a–d: cropland; panel e–h: forest; panels a, b, e, and f indicate the results derived from this study; data in panels c, d, g, and h were from LUH2-GCB; panels a, c, e, and g show the changes from 1900 to 1980, whereas panels b, d, f, and h show the changes from 1980 to 2019; negative and positive values indicate coverage reduction and increment, respectively.Full size imageThe problems of cropland area expansion reported to FAO are likely caused by changes in the underlying database, in which the Chinese Agricultural Yearbook (CAY) was used prior to 1996, the China Land and Resources Statistical Yearbook (LRSY) from 1996 to 2007, and the National Land and Resources Bulletin (NLRB) after 2007 (Supplementary Information 3).These three datasets are not consistent with each other because surveying methods were distinct. For example, cropland area in CAY before 1982 used an extrapolation method (i.e. “production-to-acreage” approach) due to limited field survey data11. Specifically, the extrapolation method was widely adopted for convenience and for taxation purposes in the early period, such as in the framework of the first benchmark cropland survey conducted in 1953. Such methods assumed that low-productivity cropland occupied an area of 1/3–1/8 of a predetermined, “standard-productivity” cropland21, which greatly underestimates the acreages of low productivity cropland. Biases accumulated in this method persisted until the satellite era (1980s), while the 1953 surveying data were used as the baseline for CAY to update cropland area on an annual basis.Besides the survey method, policies also contributed to a bias of reported cropland area. To tackle rising food demands, cropland expansion was highly encouraged by the government before the 1980s, implementing an incentive policy to allow new tax-free cropland without reporting to the government for the first 3–5 years22,23. Even after the initial reporting free period, these newly cultivated croplands continued to be unreported due to political incentives to show increasing crop yield to the local authorities23,24.When the first comprehensive and systematic survey (i.e. the second national cropland survey conducted during 1985–1996) was completed, the cropland area was found to be larger than previously reported in CAY11. Similarly, the shift from the use of LRSY to NLRB also introduced a spurious cropland area increment from 2007 to 2010 as small, fragmented croplands were identified by better technologies adopted in NLRB, which had remained undetected previously (Supplementary Fig. S10).Thus, LUH2-GCB has inherited spurious temporal signals of abrupt cropland increment in FAO from the 1980s to 2010 (Fig. 1a and Supplementary Fig. S10). Therefore, if the areas of other land cover types (e.g. forest) are indirectly constrained from cropland area change, cropland area biases were mirrored in the area change of other land use types. This is the case for the LUH2-GCB and for Liu and Tian’s previous land use gridded datasets. Our new database, rebuilt from Yu et al.11, corrected these problems in temporal dynamics by assimilating multiple data sources (Fig. 1a). More specifically, we retrospectively reconstructed information about cropland and forest areas year by year, using tabular data from official agencies (Supplementary Information 1 and Supplementary Data 1). To further reduce the aforementioned biases, we used the most recent and authoritative record of provincial cropland and forest areas available as the benchmark, and then spatialized the cropland and forest distributions using gridded maps as ancillary data (Supplementary Information 1). The area changes were also validated using inventory-based benchmark data (Fig. 1a, d, details were presented in Yu et al.11 and Supplementary Information 1.2).Changes in forest area in China also varied dramatically among databases. Based on Ramankutty and Foley19 and LUH2-GCB, a net forest loss was found from 1900 to the last available year, at 33–108 Mha whereas Liu and Tian18 and Houghton and Nassikas20 reported a net increase of 15 Mha (1900–2005) and 70 Mha (1900–2015) in forest area, respectively (Fig. 1d, e).By assimilating multiple source records, reports, and national surveys, however, our newly reconstructed and intensively validated database (Supplementary Figs. S4, S5, and S8) with corrected biases suggests that the forest area increased by 58 Mha from 1900 to 2019 (Fig. 1e). In particular, our data suggest that there is a surprisingly large underestimation of forest expansion in all other databases (38–102 Mha) after 1980 (Fig. 1f). We performed spatial analyses and show that widespread forest expansion in our reconstructed data was represented as a forest decline in LUH2-GCB during the period 1980–2019 (Fig. 2f, h). These existing biases in the dataset during the last four decades can be simply removed using recently available and spatially explicit forest products (Supplementary Table S2).Bias in forest change might be explained by two reasons. First, gridded datasets inherited and transferred errors from the use of FAO-based cropland dataset in developing global land use databases such as HYDE and thus LUH2-GCB8. Second, the FAO forest area reported is an important reference data used in these databases. The FAO forest area is reported based on a “land use” definition, which underestimated gross “land cover” change signals between reported years (Supplementary Information 1.3). Specifically, the FAO forest area describes lands that have been forested and will continue to be used for forestry (e.g. cut-over area, fired-over area, unestablished afforestation land) (Supplementary Table S5). This approach overestimates forest area by including lands used for reforestation where no forest was yet created. Thus, for example, the FAO statistics reported a 157.2 Mha forest area in 1990 (Supplementary Fig. S7), which is ~30 Mha higher than officially released data.More importantly, newly established forests were underestimated in such an accounting approach. The forest area expansion in China reported in the FAO statistics was 61 Mha from 1990 to 2019, which is 30 Mha lower than the officially released data16. Our reconstructed dataset, in agreement with officially released forest area, uses a “land cover” definition that characterizes the distribution of annually established forests. Therefore, the FAO statistics – a data set with definition specified to describe the area of land use – should be used with caution for constraining the temporal evolution of forest cover distribution in gridded data reconstruction, and the modeling community should be alerted to treat the LUCC data appropriately.Nonetheless, the FAO and the related LUH2 products were the dominant LUCC forcing data used in multiple studies3,25, including various process-model-based intercomparison projects (e.g. MsTMIP, LUMIP, NMIP, TRENDY), annually released Global Carbon Budget reports2,26, and IPCC reports5, implying a potential bias of these assessments for the China region. In contrast, changes in forest area from our database were independently developed (Supplementary Information 1.2), intensively calibrated, and validated using officially released national forest inventories (NFIs, see Supplementary Figs. S4 and S5), which can help to reduce the potential bias of C balance assessment in China. More specifically, the total forest area and PF area in our database were compared with historical NFIs released by the National Forestry and Grassland Administration at provincial level since 1949 (Supplementary Figs. S4 and S5), which supports the reliability of our reconstructed data.Historical carbon stock changesTo illustrate the bias in the C balance of China when using previous LUCC dataset, we performed simulations with the DLEM model for the period 1900–2019 at a resolution of 0.5 × 0.5 degree forced by our new LUCC dataset. We validated the distribution and changes of C stock using published studies and previously reported inventory-based estimations (Supplementary Information 6 and 7). The model could capture well C dynamics in China using inventory-based forest C stock changes at both provincial and national levels as the validation data set (Supplementary Fig. S14).Our results show that the total C stock decreased by 6.9 ± 0.6 Pg from 1900 to 1980 and increased by 8.9 ± 0.8 Pg C from 1980 to 2019 (Fig. 3, derived from experiment S1 in Supplementary Table S10). Such a large C stock increment since the 1980s, which is dominated by vegetation biomass C accumulation, was not captured in the MsTMIP and TRENDY projects driven by different versions of the LUH2 data (Fig. 3). This is attributed to the fast expansion of forest area(s) that was not captured by this land use forcing (Fig. 1).Fig. 3: Temporal changes of carbon storage from 1900 to 2010s in China.Panel a–c indicate vegetation carbon, soil organic carbon, and total ecosystem carbon, respectively. Results derived from experiment designed to have all environmental factors vary historically from 1900 to the 2010s, for model design details of this study see Supplementary Information 8); pink color: MsTMIP (1900–2010); blue color: TRENDY (1900–2019); dark color: this study (1900–2019); the shade areas represent the ranges of 1 standard deviation; unit: Pg C.Full size imageWe found that the large-scale forest expansion in China alone has caused a substantial C accumulation since 1980 (0.21 ± 0.006 Pg C per year, Table 1). In contrast, the forest C sink of the TRENDY models is negligible (−0.02 ± 0.05 Pg C per year, Table 1). A moderate C source (0.10 ± 0.08 Pg C per year, Table 1) was even found in the MsTMIP models, since these models were driven by continuous forest area loss and cropland expansion since the 1980s (Supplementary Fig. S7).Table 1 Comparison of reported carbon fluxes from various biomes in ChinaFull size tableA recent atmospheric inversion-based study reported that China’s land ecosystems were a large CO2 sink of −1.11 ± 0.38 Pg C per year27, which seems to be ecologically implausible and critically sensitive to the assimilation of the CO2 record from one station28. The compilation of previous studies from inventory- and satellite-based estimation, atmospheric inversion, and process-based models suggested that the Chinese C sink was much smaller (−0.18– −0.45 Pg C per year; Table 1). Our model-simulated terrestrial sink (~−0.28 ± 0.06 Pg C per year) was in this range (Table 1).While our simulated C balance in different categories or biomes is close to previous estimations, three major differences are observed (Table 1). First, because the LUCC data used in previous global models suffered from biases as shown above, the national C sink was generally underestimated in these simulations (Table 1). Second, our estimation of the forest sink is around two to three times larger than the previous one during 1949–199829. This was mainly because forest area was underestimated by over 33% (53 Mha) in the previous study29 compared to the national forest inventory (NFI)16. This underestimation may stem from exclusion of economic and bamboo forests. The third major difference is the role of grassland soils in C balance during the period 1980–2000. China’s grassland soils were previously reported as a minor sink of −0.007–−0.022 Pg C per year from the 1980s to the 2000s (Table 1), while our simulations suggest that grassland soils were a C source of 0.062–0.066 Pg C per year. This discrepancy lies in the approaches used and the accounting boundaries between studies (i.e. whether the transitions of grassland were considered), in which LUCC impacts were represented differently. For example, impervious surfaces (part of urbanized area) expanded into ~15 Mha of natural lands in China from 1978 to 201730, which further drove redistribution of cropland into marginal lands with the majority converted from grassland, causing wind erosion, habitat loss, and more water and fertilizer consumption31. Earlier studies using a static grassland map exclude the C stock loss in the land-use transition32. Thus, the distinct roles of grassland soils (i.e. sink vs source) derived from our simulations and earlier studies are not contradictory but are due to differences in accounting boundaries.LUCC impacts on carbon stock changesOur DLEM simulation indicates that LUCC induced a C loss of 5.1 ± 0.7 Pg C from 1900 to 2010s (Fig. 4a), which is substantially lower than that from MsTMIP (13.8 ± 7.7 Pg C, 1900–2010) and TRENDY (9.4 ± 3.3 Pg C, 1900–2019; Fig. 4e, f and Supplementary Fig. S18d, g). From 1980 onward, LUCC increased C storage by 4.3 ± 0.7 Pg C, with the major contribution from vegetation biomass C increment in the southwestern and northeastern regions (Fig. 4d and Supplementary Fig. S19a). Nonetheless, this C increase in biomass was not captured in MsTMIP and TRENDY models (Fig. 4e, f and Supplementary Fig. S19d, g), which simulated that LUCC continued to reduce C stock by 7.5 ± 1.6 and 5.3 ± 2.3 Pg C during the period 1980 to the 2010s, respectively (Fig. 4 and Supplementary Fig. S20).Fig. 4: Spatial distribution of LUCC impacts on ecosystem carbon storage.Panel a–c: LUCC impacts for period of 1900–2019; panel d–f: LUCC impacts for period of 1980–2019 (d–f). Panels a and d are from this study; data in panels b and e are from MsTMIP; data in panels c and f are from TRENDY; negative and positive values indicate sink and source, respectively; green and yellow bar stacked in the insert indicate LUCC impacts on vegetation and soil organic carbon in Pg C; spatial map unit: g C m−2; error bars: one standard deviation from the mean of LUCC impacts on total carbon storage.Full size imageTo confirm that such discrepancy was induced by LUCC data but not the DLEM model, we set up additional DLEM simulations using the LUH2-GCB database (Supplementary Information 8). The simulated C losses induced by LUCC when DLEM was driven with LUH2-GCB were 6.5 ± 0.4 and 11.4 ± 0.6 Pg C during the periods of 1980–2019 and 1900–2019, which are close to MsTMIP and TRENDY simulations. These results confirm that the LUCC forcing database is the major contributor to the difference between our simulations and the MsTMIP and TRENDY projects. An earlier study reported that global LUCC-induced C emissions are substantially underestimated due to underrepresented tree harvesting and land clearing from shifting cultivation33. Our simulation revealed that regional LUCC-induced C emission could also be overestimated in China due to a bias in the LUCC data.There are also disputes over whether the LUCC induced a C sink in China since the 1990s or not (Supplementary Table S8). By using an updated LUCC database, our simulations revealed that LUCC was a strong C sink in China, and that its magnitude was larger than previous estimates since the 1990s (Supplementary Table S8). Our results using an improved LUCC forcing data can facilitate narrowing down the well-known, large uncertainty in LUCC-induced C change at regional scale.Attributions of different factors on C stock changes since 1980By using the DLEM model with factorial simulations (see Supplementary Information 8 for details), we examined the direct and interactive contributions of different drivers to terrestrial C stock change in China for the period 1980–2019, including LUCC, climate, forest management, N deposition, and CO2 fertilization (see Methods, Fig. 5). Note that historical C stock change is not equivalent to the sum of factorial attributions as the baseline conditions differ (see Supplementary Information 8).Fig. 5: Attributions of different environmental factors on carbon stock change in China from 1980 to 2019.Panels a–c indicate attributions of impacts on the changes of vegetation carbon, soil organic carbon, and total ecosystem carbon, respectively; CLM: climate; CO2: rising atmospheric CO2 concentration; Ndep: N deposition; Man: forest management; Nfer: N fertilizer and manure application.Full size imageOverall, 81.9% (6.5 Pg C) of the terrestrial C sink during this period was attributed to direct impacts of all major factors, while the interactive effect contributed 18.1% (1.43 Pg C; Fig. 5c). Among all the factors examined, LUCC was the dominant driver accounting for 50.3% (3.96 Pg C) of the total C increment during the period 1980–2019 (Fig. 5c), which was largely attributed to biomass C accumulation (70.0%; Fig. 5a, c). Tian et al.13 reported that LUCC’s contribution to the sink in China was at 0.05 Pg C yr−1 since the 1980s – an amount that is only about 30% of our simulations. The discrepancy is attributed to the different representation of forest expansion in model simulations, which was 65 Mha from 1980 to 2005 in our database but only ~14 Mha in Tian et al.13. Similarly, the increase in the global land sink during the recent period (1998–2012) was also mainly attributed to LUCC (i.e. decreased tropical forest area loss and increased afforestation in northern temperate regions), instead of CO2 or climate change34.Climate change enhanced biomass C stocks by 1.63 Pg but caused a soil C loss of 0.30 Pg, thus contributing to land sink of 1.41 Pg C (18.0% of the total with all factors) since 1980 (Fig. 5). Other global change factors, such as N fertilizer application, atmospheric N deposition, and rising CO2, had a relatively minor contribution (0.1–9.54%) to the terrestrial C sink. Therefore, conversely to previous studies13,35,36,37, we showed that LUCC was the dominant driver of the recent land C sink in China, and other factors including climate change, rising CO2, and N deposition, contributed much less (0.1–18.0%) to the C stock increment in China (Fig. 5c). Tian et al.13 pointed out that LUCC effects in China should not be ignored and that the CO2 fertilization effect might be overestimated in Piao et al.38.Our simulations confirm these statements, and further show that LUCC was actually the largest contributor to land sink in China since 1980 (Fig. 5). In those studies which did not account for the influence of LUCC separately, the effects of other global change factors may have been overestimated by including LUCC impacts. For example, Chen et al.39 and He et al.37 attributed China’s C sink into different components including climate change, leaf area index (LAI) change, rising CO2, and N deposition. Such partition inevitably masked the separate contribution from LUCC, because LAI changes are closely related to land-cover changes. Thus, the accurate representation of the LUCC should be prioritized in future modeling attribution studies.Carbon stock changes in each land cover type since 1980The contribution of the establishment of young and new forest plantations to C sink has received increasing attention3,40,41,42. Our simulation (experiment S1, see Methods section) revealed that the increase in terrestrial C stock was dominantly contributed by biomass C accumulation (76.3%) (Fig. 5), in which the natural and planted forests accounted for 65% (2.9 Pg C) and 35% (1.6 Pg C) during the last four decades. We examined the LUCC effect (i.e. the largest contributor of C stock increment in Fig. 5) on the C stock of different biomes and confirmed that forest was the major contributor of the net C accumulation in China since 1980, while other biomes, including cropland, grassland, shrubland, and wetland, were relatively stable, varying from −0.3 to 0.3 Pg C during the same period (Fig. 6). A recent study documented that forest expansion was essential for a large C sink in southern China during 2002–2017, where newly-established and existing forests contributed to 32% and 34% of land C sink in the region43. In comparison to the large biomass C increase since 1980 (3.0 Pg C, Fig. 6a), the SOC increase was much lower (0.7 Pg C) during the concurrent period, although SOC changes in each biome varied greatly (–3.4–8.6 Pg C; Fig. 6b) due to area change from land conversions. The biome-level analyses further revealed that the LUCC-induced C stock increment was dominantly contributed from forest and by area expansion, while C storage in grassland and shrubland was reduced by LUCC (Fig. 6).Fig. 6: LUCC-induced carbon storage changes by land cover types based on model simulations during 1980–2019.Panel a–c indicate vegetation carbon, soil organic carbon, and total ecosystem carbon, respectively; the widths of the red blocks indicate the estimation ranges of net changes in model simulations; purple error bars indicate one standard deviation of multiple model runs; negative and positive changes indicate carbon loss and gain, respectively.Full size imageThis study highlights the dominant role of LUCC in determining the terrestrial C sink in China. Because of inaccurate representations of land cover change in China, previous estimates of the terrestrial C sink have been strongly underestimated. In contrast, forest expansion and cropland abandonment have been overestimated in the U.S., resulting in an underestimated C emission since 19807. Hence, we highlighted that the global LUCC database should be further improved, which could potentially narrow down the C imbalance reported in global C budget accounting2. In contrast to the previous studies, we showed that the contributions of factors including rising CO2, N deposition, and climate change to the land C sink in China were much smaller than LUCC over the past four decades (1980-present time). Thus, reforestation projects could represent important climate change mitigation pathways, with co-benefits for biodiversity33. To achieve the ‘C neutrality’ goal as the Chinese government declared, future climate policy should be directed to improve land management, especially forest ecosystems.Implications for future LUCC data improvementsThis study provides a novel reconstruction of recent land use change in China and assesses its implications in quantifying for terrestrial C storage dynamics. The improved dataset more accurately depicts the spatiotemporal dynamics of LUCC in China because the historically contradictory surveying records were identified, which helped to correct the biased temporal signals. Specifically, the improved surveying methods and the socioeconomic factors have greatly shaped the LUCC signals. We advocate that these impacts should be considered in the reconstruction of the national and global LUCC dataset, especially in the areas that have been intensively disturbed by human activities as is the case of China. These endeavours will be worthwhile, as demonstrated by the large impact that these bias corrections have on China’s C dynamic assessments since 1900. Thus, accurate delineation of LUCC forcing should be stressed in global simulations, including C budget accounting, biodiversity assessments, and ecosystem services evaluations. More

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Chocolate, a serious contender for the world’s most beloved food, is made from the seed kernels of the cacao tree (Theobroma cacao). But despite its popularity, Justine Vansynghel at the University of Würzburg in Germany and her colleagues found that nobody had quantified how species living on small-scale cacao farms collectively affect production1.

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doi: https://doi.org/10.1038/d41586-022-02908-0

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Cliffs present a unique flora that has been little studied until now mainly because of the inherent difficulties to access this unique environment, as shown in Fig. 2. The techniques currently used to access plants on steep slopes and cliffs (e.g., abseiling, helicopter) are generally dangerous, costly and time consuming. Using a small aerial manipulator to sample plants on the cliffs can represent many advantages, including safety and portability, as well as the capability of reaching otherwise inaccessible locations easily, quickly and at low cost.Figure 2Examples of the cliff habitats of some critically endangered species on the Kauaʻi Island along with the count of known individuals as of February 2022.Full size imageHowever, several technical challenges make it difficult to develop suitable aerial manipulators for this task. Indeed, the sampling of plants on cliffs necessarily leads to significant collision risks, as well as contact forces and moments during sampling that can destabilize the drone. The samples collected would also need to be accessed from the side of the aerial platform22. Any weight (e.g., sampling tool, collected samples) located horizontally away from the center of mass of the drone creates large additional demands on the propulsion system of most drones. To collect specific plant parts in windy conditions (e.g., scion, flowers, seeds, etc.), precise and fast motion is required even in degraded Global Navigation Satellite System (GNSS) coverage near the cliffs. The great diversity of plant species and morphology found on cliffs, as well as the variety of targeted sections of plant, also represent a major design challenge. Finally, to maximize the adoption of this tool, it is also desirable that scientists with minimal training could use this platform. The next sections describe how these challenges were addressed through the development of the Mamba.Suspended sampling platformThere are a multitude of configurations that could have been explored to sample plants on cliffs. Some drones have manipulators rigidly attached to their structure20,23. However, these manipulators tend to have a limited reach to keep the center of mass within the propeller footprint and to minimize the inertia of the system. This could result in a high collision risk with the propellers in the uneven terrain found on cliffs. The contact forces created during the sampling operation also generate destabilizing moments through manipulators rigidly attached to the drone. To address these challenges, concepts involving a compliant manipulator operated from specialized drones were also explored10. Alternatively, some aerial manipulators were also passively suspended under the drone through a long rod21,24. This keeps the drone above potential obstacles within the environment, significantly reducing the operator’s mental demand and stress while also reducing the disturbances transmitted to the drone to a downward force aligned with the rod and yaw torque. To maintain these advantages while providing better precision, some projects have developed cable suspended platforms equipped with thrusters25,26. As these platforms do not have to counter gravity, the thrusters can be positioned to fight external disturbances more efficiently (e.g., wind, contact forces, drone movements). Existing systems however only stabilize the suspended platform close to its equilibrium point.The chosen concept for the Mamba, illustrated at Fig. 3, consists of a suspended platform that can stabilize itself far from its natural equilibrium to provide a large workspace. The lifting drone in this system stays safely away and above from steep cliff faces, while supporting the platform and providing rough positioning in space through better GNSS coverage. The platform is suspended 10 m below the lifting drone using four attachment points to prevent pitch and roll motions. The cable also acts as a low pass filter, isolating the platform from the fast drone movements required to fight wind disturbances. The suspended platform design can then focus on fast and precise positioning, while also being tolerant to contacts during sampling. To do so, four pairs of bidirectional actuators are used to control the motion in the plane of the pendulum (i.e., x and y translation, as well as yaw). Two pairs of actuators are installed in the x-direction to provide sufficient force to reach plants as far as 4 m from the equilibrium position. This corresponds to roughly 3.3 m from the tip of the lifting drone’s propellers.Figure 3(a) General concept of the Mamba and lifting drone during transit and sampling on cliffs. (b) Side view of the Mamba showing the components and cable installations. (c) Top view showing the antagonist thrusters configuration. (d) Close-up of the sampling tool and 2 degrees of freedom (DOF) wrist specifically designed to sample small fragile plants.Full size imageSince the Mamba is self-powered and has its own communication system, the lifting drone function is simply to lift the platform and hold it in place. This made it possible to select amongst the many commercially available products to accelerate the development of the Mamba. The DJI M300 was chosen as it comes equipped with a 360° optical obstacle avoidance vision system, an IP45 rating, and a flight time of 20 min with the Mamba attached (3.3 kg). It also advertised a four constellation GNSS receiver for better coverage around buildings, structures, and cliffs.Precise control in windsWinds under 20 km/h represent a gentle breeze on the Beaufort scale. At this level, the wind only moves the leaves, and not the branches, which allows for ideal sampling conditions. According to historical weather data from 2020, daily maximum winds are less than 20 km/h for 40 to 70% of the year, depending on the exact location on Kauaʻi Island (i.e., Lihuʻe International airport, as reported by the National Oceanic and Atmospheric Administration, and the Makaha Ridge Weather Station, as reported in the MesoWest database). This also implies that Kauaʻi experiences stronger winds on certain days which would make precise sampling difficult. Wind conditions are also more challenging near cliff faces, with increased turbulence and vertical airflow along the cliff.To allow operations on most days, while providing precise positioning and fast rejection of wind disturbances, the actuators of the Mamba are oriented in the horizontal plane. This allows the actuator forces to directly affect the motion of the suspended platform. Each actuator of the Mamba consists of a pair of brushless DC motors and 23 cm propellers capable of producing 7 N of force. The motors are installed in opposite directions, are always idling at their minimum rotation speed, and are commanded to only create force in their preferred direction. This antagonistic configuration avoids the low-velocity dead zone of a brushless motor during thrust reversal. This makes it possible to quickly revert the direction of the thrust and nearly triples the bandwidth of the actuators to approximately 2.5 Hz27. This configuration, however, comes at the expense of added mass and components.The Mamba is equipped with a flight controller that includes a control system, and a state estimator. To avoid degraded GNSS coverage issues, the state estimator only uses data from a high accuracy inertial measurement unit (IMU) to estimate the attitude of the platform. This provides the relative position of the platform with respect to the drone and is sufficient for teleoperation. Three separated proportional-derivative controllers are used for each of the DOF controlled by the actuators. This control system also provides attitude-hold assistance (i.e., pitch and roll, which correspond to x and y displacements, as well as yaw). This implies that if the user does not send any commands, the suspended platform maintains its current state.Figure 4 illustrates the stabilization accuracy of the Mamba when moving along a representative trajectory when suspended indoors from a 5.7 m cable (limited by ceiling height). This experiment confirmed that the sampling tool can maintain a position at a horizontal reach of 2.25 m with a precision of about 5 cm for 30 s. As the horizontal reach and precision are limited by the cable angular displacements (e.g., component of weight acting on the pendulum, IMU angular resolution), the resulting workspace when operating with a 10 m long cable would reach a radius of 4 m with a positioning accuracy of about 9 cm. To account for potential external disturbances like wind, the sampling tool was designed with an opening of 15 cm. This creates some margin for the pilot to align the target with the sampling mechanism. Field trials detailed below demonstrated that the Mamba actuators and controller could maintain a sufficiently stable position to sample plants in winds During the sampling phase, wind speed averaged 15.7 km/h with a standard deviation of 6.8 km/h, while wind gusts reached an average of 20.1 km/h with a standard deviation of 6.5 km/h. The maximum average wind speed recorded during sampling was 28 km/h with gusts up to 37 km/h. This represents a lower bound of the system performance, as no failure resulted from the wind conditions experienced during the trials. The a ttached Supplementary Video also demonstrates the stability of the system.Figure 4Representative motion of the sampling tool within its workspace based only on feedback from a high accuracy IMU and recorded using a motion capture system. The natural equilibrium point is at (0,0). The experiment starts with a 90° rotation around the z axis, followed by a forward movement along the x-axis of the Mamba and a lateral movement along its y-axis. The system then maintains this position for 30 s without any user inputs. Produced in MATLAB R2021a.Full size imageTeleoperated sampling of cliffs habitatsPlants growing on Kauaʻi cliffs exhibit a wide morphological variety. For this project, targets ranged from small herbaceous plants such as Euphorbia eleanoriae (plants  More

Intra-bull semen quality variationTo understand variation in bull semen quality, we assessed 1271 ejaculates from 79 different bulls (11 different breeds) housed at Rockhampton stud farm, in the state of Queensland, Australia, over a period of 5 years (2014–2018). The raw data, together with the semen analysis and when the samples for each individual bull were collected is available in Supplementary 1. The climate in this area (23.3786° S, 150.5089° E) is considered sub-tropical, ranging from 16 °C in winter to over 30 °C in summer. A comprehensive semen analysis was undertaken, including sperm morphology and motility. To determine the variation in semen quality, we plotted the percentage of sperm normal forms for each bull that had 5 or more ejaculates taken annually. Morphology was used as a measure of sperm quality, as Söderquist et al.17 demonstrated that sperm motility is heavily influenced by the collection/collectors and, therefore potentially unreliable and irreproducible. This resulted in the analysis of 1178 ejaculates from 50 bulls, with an average of 23 ejaculates per bull. The percentage of sperm normal forms as a box and whiskers plot for each bull is given (Fig. 1). As shown, many bulls demonstrated extremely high variation between ejaculates, with several males ranging from  70% (considered an outright “pass” in terms of cryopreservation potential) of normal sperm morphology. On the contrary, some bulls appeared to produce consistent semen samples across the year.Figure 1Changes in sperm normal forms. Semen samples were taken from bulls via electroejaculation and the percentage sperm normal forms were counted. The data show a box and whiskers plot consisting of 50 bulls, each of which had at least 5 different ejaculates across a minimum one month. Each box and whiskers plot represents an individual Bull showing the median, upper and lower quartile range. Outliers are represented by individual dots.Full size imageTo determine the amplitude and the proportion of bulls demonstrating variation in the number of normal sperm forms, we measured the difference between the maximum and the minimum values recorded for each animal. From this analysis we found that: 9 (18%) bulls showed less than 20% variation in normal forms; 15 (30%) bulls had between 20–40% variation; 13 (26%) bulls were between 40–60% and for 13 (26%) bulls this number was over 60%. These data have major implications when interpreting semen analysis, since a bull could be classified as either fertile or infertile depending on which ejaculate was considered. This data also sheds light into why correlations between the vBBSE parameters such as morphology and the bull fertility are so variable.Seasonal effect on semen qualitySeveral sources of environmental influence have been suggested to affect bull sperm quality. These include feed availability (i.e., higher conception rates in rainy seasons)27, excessive protein intake28, day length29, thermal heat stress and age30,31. To better understand the dynamics of semen quality variation within our samples, we plotted sample “pass” and “fail” cryopreservation criteria against the month of collection. A raw bull semen sample is classified as “pass” when motility is above 60% and normal forms greater than 70%. When samples were between 30 and 60% motility and 50–70% normal forms, they were classified as a “compensatory” (or qualified) pass (q-pass). The compensatory pass relied on there being the ability to have at least 10 million motile normal forms of spermatozoa in each straw to allow for conception. An outright failure was given to any sample with less than 30% progressive motility or 50% normal forms. This allowed each ejaculate to be placed into a binary “pass” or “fail”.The data for the percentage of total males that “failed” within each month (1271 ejaculates) is shown (Fig. 2A). Clearly, there is a seasonal pattern, with over 90% pass rate in winter (June–August) that fell to 50% or lower in summer (Dec-Feb). Considering that all bulls were greater than 4 years old, housed on the same stud farm and received the same dietary supplement we found no relationship in terms of “pass” or “fail” rates to these parameters. Thus, the data clearly suggested that Temperature/Temperature-Humidity or day length were responsible for the increased failure rates seen during Summer. Therefore, to understand if there was any causal relationship, we correlated either the average monthly temperature (Fig. 2B) or daylight (Fig. 2C) with monthly failure rates. The data showed a correlation with monthly temperature (r2 = 0.55; and temperature-humidity index – see further modelling below) but not with daylight hours (r2 = 0.05). Combined, these data suggest that temperature was the most likely reason for increased failure rates during the warm/hot months.Figure 2Seasonal variation in the semen quality of 1271 bull semen ejaculates. Semen samples were taken from bulls via electroejaculation and a full semen analysis was undertaken. Each sample was then classified as a pass or fail as described in Materials and Methods. (A) The percentage failure rate for each month is shown for all bulls. The number above each column indicate how many semen ejaculates were processed that month. (B). Scatter plot showing the average monthly temperature of Rockhampton and the percentage of samples that fail/month. Line of best fit indicates and r2 = 0.55. (C) Scatter plot showing the average daily sunlight in Rockhampton and the percentage of samples that fail/month. Line of best fit indicates and r2 = 0.04.Full size imageChanges in normal sperm forms categorised by breedThe present study investigated 11 different breeds of cattle, and we reasoned that maybe one, or more breed(s) contributed to failure rates more than others. Therefore, we plotted the percentage of normal forms for every ejaculate against the breed (Fig. 3). All breeds showed similar variation except for the Belmont Red, Boran and Wagyu. However, a relatively small number of bulls from the Belmont Red and Boran breeds were assessed in this study, therefore, it is unclear if they are indeed more resistant to heat. In the case of the Wagyu, it is worth mentioning that only one animal exhibited poor sperm morphology in several ejaculates (Fig. 3 circled) during winter. A close inspection of the records showed that during this time the animal had a fever episode, with body temperature reaching 39.4 °C, and that the sperm morphology returned to normal in approximately 70 days.Figure 3Variation in Semen quality as judged by Bull breed. Semen sample was collected and analysed for sperm morphology. The animals were then separated according to breed and the percentage normal forms for each ejaculate are shown.Full size imageSome bulls are heat-sensitive, whilst others are heat-tolerantAnalysis of the present data clearly illustrated that some bulls showed marked variation in terms of their semen quality throughout the year (Fig. 1). Meanwhile, others demonstrated much less variation, and were reasonably consistent. To further clarify these differences, we closely analysed the percentage of sperm morphology from two bulls, both of whom had several ejaculates were taken throughout the year, including during and after summer (Fig. 4). There was a clear pattern, and evidence of two types of bulls. Prior to the summer season, bull 1 (Fig. 4, red), designated here as “heat-sensitive”, exhibited  > 70% normal forms of spermatozoa. This value decreases dramatically, reaching its lowest point (10%) mid-January, before undergoing a recovery by April ( > 70%). In contrast, bull 2 (Fig. 4, green) showed a consistent semen profile throughout the year. The data suggest this bull was more “heat-tolerant”.Figure 4Identification of Heat-Sensitive and Heat-Tolerant bulls. The percentage normal sperm morphology from two bulls, both Droughtmasters, which had several ejaculates taken over the course of the year were plotted against the month in which the semen sample was taken. The first bull (red) is an example of a heat-sensitive bull. The second bull (Green) an example of heat-tolerant response.Full size imageTo further explore the concept of “heat-tolerant” and “heat-sensitive” bulls, we subjected 20 Wagyu bulls to a single event of controlled heat stress (40 °C, 12 h). This experiment was performed during Winter, at Singleton (New South Wales, Australia, 32.5695° S, 151.1788° E), where the average temperature was 17 °C and never exceeded 18 °C. Prior to the heat stress event, baseline semen samples were taken from each animal. After heat stress, semen samples were taken every week for 11 weeks. During the experiment, two bulls were removed from the program due to infection and sickness whilst a 3rd bull was removed as it refused to co-operate with electroejaculation procedure. From the remaining bulls, we were able to reproduce the heat-sensitive and heat-tolerant bull phenomenon. The raw data from this work is given in Supplementary 1, and an example of the data is shown (Fig. 5). For 14 bulls, we found no difference in terms of their baseline samples, which were between 70–90% normal forms. This is consistent with the Wagyu bull characteristics and their heat-tolerance (Fig. 5, yellow, green, blue lines). Within these “heat-tolerant” bulls, there was a variation of 16–22% sperm normal forms. For the other three bulls, two of them showed a decline in sperm quality, which began 2–3 weeks after the heat event, dropping from a baseline of 85% and 90% normal forms to 55% and 59%, respectively (30–31% variation in normal forms; Fig. 5, grey and orange line). The third bull showed a greater degree of heat-sensitivity. Starting at 77% morphologically normal sperm, the spermiogram of this bull illustrated a rapid decrease in normal forms in a short time (2 weeks), reaching around 40% after 4–5 weeks. Sperm morphology remained at this level (37% variation in normal form) for four weeks, before recovery. These data show that under experimental condition, the phenomenon of heat-sensitive and heat-tolerant animals can be reproduced. Further, it appears that there are degrees of heat-sensitivity.Figure 5Heating of Wagyu bulls to identify heat-sensitive and heat-tolerant effect. Twenty Wagyu bulls all 3 years of age and over were heated to 40 °C for 12 h in an insulated barn. Before heating, bassline samples were taken (week 1). After heating, electroejaculation was used to collect semen every week for 11 weeks. For every sample, sperm morphology was counted by a qualified theriogenologist. The data show the percentage normal morphology for 5 bulls. The light blue line indicates a heat-sensitive bulls, whose morphology was affected by heat, then returned back to baseline. The orange and grey line represent two related bulls (same father) who also produced less than 70% normal forms. The yellow, green and dark blue lines represent three heat-tolerant bulls, whose semen profile did not drop below the 70% normal spermatozoa threshold.Full size imageEnvironmental heat stress leads to poor sperm quality 17 days laterSimilar to previous reports, we noted that sperm quality does not begin to deteriorate until 2–3 weeks after the heat stress event of the bulls32. Based on the timing of spermatogenesis, this is consistent with reports that meiotic cells are more susceptible to heat stress following a heating event, with poor quality spermatozoa appearing in the ejaculate around 2–3 weeks later. To better understand the relationship between a “heat-event” and the production of poor-quality spermatozoa, we modelled both maximum temperature and maximum temperature humidity index (THI) and their relationship to the proportion of morphologically normal spermatozoa. The THI is an index representing the effect of humidity on the heat stress of an animal. THI was obtained using the following formula:$$mathrm{THI}=0.8* frac{{T}_{max}}{100}+frac{left(humidity*left({t}_{max}-14.4right)right)}{1}+46.4$$where Tmax = maximum temperature, (oF), and H = relative humidity.We plotted the correlation between semen quality and Tmax on the day, and every day prior (up to 40 days) to semen collection (Fig. 6). This modelling demonstrated that poor semen quality was due to maximum daytime temperature 17 days prior (Fig. 6a, arrow). Notably, 1 day of heat-stress appears to be sufficient to cause poor sperm quality, since if we take the average of 2 (Fig. 6b) or 3-day maximal temperatures prior to collection (Fig. 6c) the correlation patterns were similar. Supplementary 3 shows further modelling for Tmax and THI using between 1 and 5-day average temperatures prior to semen collection.Figure 6Bull semen quality (as percentage sperm normal forms) is related to the temperature that occurred 17–19 days ago. Correlation between sperm quality and maximum Temperature (Tmax). The Y axis is the Pearson correlation coefficient and X axis represents the number of days before the day the sperm sample was taken. (a) Uses one day of Tmax data whilst (b) averages two and (c) averages three consecutive days of Tmax data. The arrow shows the best correlation between Tmax and poor sperm quality, which occurs around 17–19 days before the semen sample is collected.Full size imageUnderstanding the temperatures at which heat-sensitive bulls failTo determine the Tmax at which bulls in the paddock begin to produce poor quality spermatozoa, we modelled data using both parameters measured at 17 days prior to the heat event, and plotted samples from 12 heat-sensitive bulls (6 Brahmans, 4 Drought Masters and 2 Santa Gertrudis). The relationship between sperm morphology and Tmax 17 days prior to heat even was plotted, with a spline smoothing cure to show the mean quality as a function of Tmax (Fig. 7a). 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Test animals and husbandryWild type adult zebrafish (AB line) were initially obtained from China Zebrafish Resource Center (CZRC, China) and reared at the zebrafish facility of the University of Saint Joseph, Macao. Fish were maintained in 10 L tanks in a standalone housing system (model AAB-074-AA-A, Yakos 65, Taiwan) with filtered and aerated water (pH balanced 7–8; 400–550 μS conductivity) at 28 ± 1 °C and under a 12:12 light: dark cycle. Animals were fed twice daily with live artemia and dry powder food (Zeigler, PA, USA). The fish used in this study were 6–8 months old, both males and females (1:1), with a total length of 2.2–3.1 cm. The total number of specimens tested was 30 for the auditory sensitivity measurements and inner ear morphological analysis (6 fish per experimental group), and 78 for the Novel Tank Diving assay (15-18 fish per group).All experimental procedures complied with the ethical guidelines regarding animal research and welfare enforced at the Institute of Science and Environment, University of Saint Joseph, and approved by the Division of Animal Control and Inspection of the Civic and Municipal Affairs Bureau of Macao (IACM), license AL017/DICV/SIS/2016. This study was conducted in compliance with the ARRIVE guidelines60.Noise treatmentsPrior to acoustic treatments, all subjects were transferred to 4 L isolation glass tanks that were placed in a quiet lab environment (Sound Pressure Level, SPL: ranging between 103 and 108 dB re 1 μPa) for a minimum of 7 days. These tanks had no filtering system but were subject to frequent water changes, and the light, temperature and water quality were kept similar to the stock conditions. This adaptation period was important to reduce potential effects of noise conditions from the zebrafish housing system.After this period, groups of six zebrafish were transferred into separate acoustic treatment glass tanks (dimensions: 59 cm length × 29 cm width × 47 cm height; 70 L)—Fig. 1 Supplementary, where they remained 24 h in acclimation. Each tank was equipped with an underwater speaker (UW30, Electro-Voice, MN, USA) housed between two styrofoam boards (dimensions: 3 cm thick × 29 cm width × 47 cm height) with a hole in the centre, positioned vertically in one side of the tank. Another similar sized board was positioned in the opposite side of the tank and fine sand was placed in the bottom to minimize transmission of playback vibrations into the tank walls. Each treatment tank was mounted on top of styrofoam boards placed over two granite plates spaced by rubber pads to reduce non-controlled vibrations.Four acoustic treatment tanks were prepared for this study to be used alternately between trials and cleaning procedures, but only two were used simultaneously. When two tanks were being used, one contained specimens under acclimation and the other fish under a specific acoustic treatment. The tanks were housed in a custom-made rack and placed at least 1 m apart to minimize acoustic interferences. The tanks were used randomly for the different treatments across the various trials.The speakers were connected to audio amplifiers (ST-50, Ai Shang Ke, China) that were connected to laptops running Adobe Audition 3.0 for windows (Adobe Systems Inc., USA). After the acclimation period, specimens were exposed to white noise playbacks (bandwidth: 100–3000 Hz) at 150 dB re 1 µPa for 24 h, starting in the morning between 10 and 11 a.m. The bandwidth adopted covered the best hearing range of zebrafish27, as well as the frequency range of most anthropogenic noise sources, such as pile driving and vessels2.Sound recordings and SPL measurements were made with a hydrophone (Brüel & Kjær type 8104, Naerum, Denmark; frequency range: 0.1 Hz–120 kHz, sensitivity of − 205 dB re 1 V/μPa) connected to a hand-held sound level meter (Brüel & Kjær type 2270). Noise level was adjusted with the speaker amplifier so that the intended amplitude (LZS, RMS sound level obtained with slow time and linear frequency weightings: 6.3 Hz–20 kHz) was achieved at the centre of the tanks before each treatment. A variation in SPL of ±10 dB was registered in the closest and farthest points (in relation to the speaker). The sound spectra of the noise treatments were relatively flat similar to the setup described in a prior study by Breitzler et al.27.Moreover, the acoustic treatments were calibrated with a tri-axial accelerometer (M20-040, frequency range 1–3 kHz, GeoSpectrum Technologies, NS, Canada) with the acoustic centre placed in the middle of the tank. The sound playback generated was about 120 dB re 1 m/s2, with most energy in the horizontal axis perpendicular to the speaker, which was verified based on previously described methods using a MATLAB script paPAM16.In this study four sound treatments were used with varying temporal patterns similar to Sabet et al.18—Fig. 1: continuous noise (CN); intermittent regular noise with a fast pulse rate—1 s pulses interspersed with 1 s silence (IN1,1); intermittent regular noise with a slow pulse rate—1 s pulses interspersed with 4 s silence (IN1,4) and intermittent random noise—1 s pulses interspersed with 1, 2, 3, 4, 5, 6 or 7 s silent intervals in randomized sequence (RN1,7) leading to a mean interval of 4 s. All intermittent patterns had 5 ms ramps to fade in and fade out pulses for smooth transitions. In the “control” treatment tank, the amplifier connected to the speaker was switched on but without playback.After each treatment, two specimens were tested for audiometry, two were tested with the NTD assay and another two were euthanized and dissected for inner ear morphological analysis.Auditory sensitivity measurementsAuditory Evoked Potential (AEP) recordings were conducted immediately after noise treatments. The AEP recording technique adopted followed previously described procedures27. The recordings were conducted in a rectangular plastic tank (50 cm length × 35 cm width × 23 cm height) equipped with an underwater speaker (UW30) positioned in the bottom and surrounded by fine sand. A custom-built sound stimulation system with enhanced performance at lower frequencies ( More