Ecology

Parasites have frequently been observed on the gills of coleoid cephalopods during ROV dives in the mesopelagic waters of the Monterey Submarine Canyon. Here, we demonstrate that at least two parasite species can be distinguished from ROV-collected specimens. Based on morphology, the first parasite was identified as the protist Hochbergia cf. moroteuthensis. Although the original description of H. moroteuthensis struggled to assign a taxonomic rank, the authors noted that the presence of trichocysts and an apical pore bear similarities to those of dinoflagellates in an encysted life stage29,30. Using Sanger sequencing and dinoflagellate cyst-specific primers, we confirm this parasite to be a dinoflagellate that forms a sister group to members of the Oodinium genus. The second parasite could not be matched to any documented morphological descriptions, and DNA barcoding was only able to resolve a short sequence that does not provide for a reliable identification.Hochbergia moroteuthensis appears to be a common parasite of midwater cephalopods and has previously been collected off the gills of twenty cephalopod species29,30. These include five taxa investigated here (C. calyx, V. infernalis, Galiteuthis spp., Gonatus spp. and Japetella diaphana), with Taonius sp. new to the list. While H. cf. moroteuthensis found in this study was somewhat smaller than the type series (0.5–1.4 mm versus 1.19–1.99)30, it was within the range of those reported by McLean et al.29 on the squids Stigmatoteuthis dofleini Pfeffer, 1912 and Abralia trigonura Berry, 1913 (i.e. 0.56 to 1.10 mm on average in length)29. The latter authors noticed that parasite size, color (i.e. white to yellow) and thecal plate morphology may differ between host species, which could indicate multiple Hochbergia species. It should, however, be noted that it is unknown whether H. moroteuthensis maximum growth is dependent on host size or whether the investigated parasites were simply in different growth stages given the study’s relatively small samples sizes. Although we did not compare H. cf. moroteuthensis morphology across hosts in great detail, the partial 18S rRNA sequences obtained for parasites on Gonatus berryi and Chiroteuthis calyx were identical. Further research is therefore warranted to investigate species-specific parasite differences and speciation among hosts.The genetic relatedness between H. cf. moroteuthensis and its Oodinium sister group is further supported by several morphological features. First, the lack of distinct dinoflagellate characters, ovoid shape and the presence of trichocysts, have also been noted for Oodinium cysts41,42,43. McLean et al.29 further reported that the nucleus of the single-celled H. moroteuthensis cyst contains diffuse chromatin, a feature unlike most dinoflagellates that possess well-defined rod-like chromosomes42. Remarkably, dinoflagellates within Oodinium are known to alternate between both non-dinokaryotic and dinokaryotic nuclei within their life cycles, which could explain H. moroteuthensis’ diffuse chromatin42,43. Similarities between H. moroteuthensis and Oodinium further extend to the parasitic life style with primarily pelagic hosts. Dinoflagellates in the Oodinium genus are all known to be ectoparasitic, infecting ctenophores, chaetognaths, annelids, larvaceans and a hydromedusa41,43,44,45,46.In spite of these similarities, there are also several noteworthy morphological differences between H. moroteuthensis and members of the Oodinium genus. Young Oodinium cysts generally have a white to yellow coloring, with older cysts taking a yellow–brown or dark brown tint41,43,44. Oodinium cysts also possess relatively simple thecal plates and above all, have a distinct peduncle, or stalk, with which they attach to the host and which is thought to serve as feeding apparatus41,43,47. Maximum lengths for Oodinium cysts have been reported up to 0.39 mm43,46. In contrast, cysts in H. moroteuthensis possess a white to yellow coloring, an intricate pattern of triangular plates, reach sizes up to 1.99 mm long, and have a simple holdfast area with an oval aperture that likely anchors them to the host30. Currently, both Oodinium and Hochbergia form a genetically distinct clade within the Dinophyceae and analysis of further specimens and genetic markers might provide more insight into their relatedness and specialization on primarily pelagic hosts. Additionally, analysis of fast- and slow-evolving genetic markers might resolve the polytomy observed in the phylogenetic trees, which were also present in the phylogenetic reconstruction of the DINOREF reference database by Mordret et al.32.The genetic similarity of H. cf. moroteuthensis to an unidentified eukaryote from the water column and the fact that we encountered the protozoans in an encysted stage, strongly suggests that these dinoflagellates infect their cephalopod hosts through a free-living life stage. Many parasitic dinoflagellates, including Oodinium, alternate between a motile free-living stage—the dinospore—that forms a vegetative feeding stage—the trophont—upon attachment to the host41,47,48. During this vegetative stage, the trophont grows greatly in size but without cellular division. Once mature, the trophont detaches from the host to divide into multiple flagellated dinospores. The dinospores disperse into the water column, free to infect new hosts (Fig. 6)41,47,48.Figure 6Theorized life cycle of Hochbergia moroteuthensis. (a) The vegetative trophont (feeding life stage) grows without cellular division on the cephalopod’s gills. (b) The mature trophont detaches and (c) divides into motile dinospores, (d) free to infect new hosts in the water column. Illustration (b) trophont adapted from Shinn & McLean30.Full size imageSuch a free-living life stage is consistent with H. moroteuthensis’ wide geographic distribution. Free-living dinospores are easily dispersed by ocean currents, and observations in both the North Pacific Ocean and the Gulf of Mexico could indicate large-scale ocean connectivity, potentially beyond the distribution reported here29. This dispersal may also offer H. moroteuthensis a wide range of infection possibilities and explain why trophonts are found in twenty-one different cephalopod taxa. Nevertheless, population genetic structure needs to be investigated, as it is currently unknown if the parasites represent multiple species.Free-living dinospores might also explain H. moroteuthensis’ location on the exterior gill tissue. With dinospores free in the water column, the fastest pathway to a cephalopod’s interior is through ‘inhalation’. In this process, cephalopods actively force water through their gills, making these the first organs Hochbergia would encounter. Respiratory organs give direct access to the cephalopod’s blood stream, and therefore offer a suitable environment (i.e. nutrient and oxygen rich) for development into a trophont. Gills also provide interstices that could simply trap dinospores. Either way, there was only one occasion (i.e. out of 355) where trophonts were seen on other body parts besides the gills (Fig. 4e). In comparison, several Oodinium parasites are also known to attach to specific host-body parts, apparently preferring sites involved in locomotor movement. For instance, Oodinium jordani McLean & Nielsen, 1989 is known to attach to the fin of the chaetognath Sagitta elegans Verrill, 187346, while O. pouchetti is mostly found on the tail of appendicularians41, and Oodinium sp. collected off various ctenophores appears to prefer attachment close to or within the beating comb rows44. Whether these surface areas offer highest encounter rates or provide a physical benefit such as enhanced oxygenation remains unknown.The increased prevalence of H. cf. moroteuthensis observed in the most abundant cephalopod, Chiroteuthis, and in the other adult cephalopods is in line with infection dynamics known from other wildlife parasites, where the probability of a parasitic infection increases with host density and age49,50,51. Following this, dinospores in the Monterey Submarine Canyon have more opportunities to encounter common squids like Chiroteuthis52 and longer-lived cephalopods. Alternatively, it is possible that the increased parasite load in adults is simply the result of larger gill surface areas when compared to juveniles. However, when comparing prevalence between host species, it should be noted that the maximum adult sizes for C. calyx (up to 100 mm in mantle length, ML) are smaller than those of Galiteuthis (500 mm ML), Taonius (660 mm ML) and Japetella (144 mm ML) among specimens found in the Monterey Submarine Canyon53,54.Other factors that might explain the observed prevalence include parasite preferences for host physiology (e.g. respiration rates) or confinement to a certain depth range18. Although Chiroteuthis, Galiteuthis, Taonius and Japetella partially overlap in their depth distributions, Chiroteuthis generally remains above the core of the oxygen minimum zone, located around 700 m in Monterey Bay52,55. Galiteuthis, on the other hand, has a bimodal distribution, with older individuals known to migrate below the oxygen minimum core52,55,56. If dinospore viability is restricted to more shallow depths, the probability of infection for Galiteuthis could decrease when living at deeper depths. This is further supported by Taonius, which showed a comparable bimodal distribution to Galiteuthis52 and shared a similar parasite prevalence. Furthermore, Japetella is the deepest living cephalopod investigated and harbored relatively few Hochbergia trophonts. In spite of this, it is unknown how long it takes for H. moroteuthensis dinospores to develop into mature trophonts and over what time frames they may accumulate on their hosts. Lab-based experiments with Oodinium sp. on the ctenophore Beroe abyssicola Mortensen, 1927 showed that trophonts needed approximately 20 days to grow from 35 µm in length to their mature size of 350 µm at 10 °C44. Given that H. moroteuthensis can grow over five times larger and lives at colder temperatures depending on its host distribution, growth periods may be substantially longer.When looking at the prevalence of H. cf. moroteuthensis over time, only Taonius appeared to be showing an increase in infected individuals over the years. Present results, however, are insufficient to determine whether this increase is the result of environmental change or part of natural variability. We therefore recommend continued monitoring to determine long term trends. Based on the monthly prevalence, it is likely that Chiroteuthis acts as a reservoir for Hochbergia parasites throughout the year. Galiteuthis, Japetella and Taonius show more seasonal dynamics. It may be that the reported seasonality is related to upwelling events or environmental cues promoting dinospore formation (e.g. increasing temperatures)50. Alternatively, cephalopods might be more susceptible to infections in certain months, or have higher resistance in others. Taonius, for example, had a markedly lower parasite load on average than Galiteuthis despite similar prevalence estimates (Tables 1 and 2), potentially indicating some sort of resistance mechanism. More research is warranted to confirm any host resistance and the influence of depth or seasonal effects.The other parasite type found in ROV-collected specimens of Vampyroteuthis infernalis and Gonatus spp. needs further characterization. Although DNA barcoding was able to resolve a short sequence that potentially places it within the phylum Apicomplexa, it appears more likely that this genetic material originated from contamination with a different parasite. Apicomplexa reported in cephalopods generally infect the digestive tract and are morphologically different from the parasites observed here19.In conclusion, our findings highlight the need for further investigation of cephalopods and their gill parasites. Considering that parasites influence biodiversity and that cephalopods form key links in pelagic food webs, future research should be focused at assessing potential effects on cephalopod physiology. For example, if H. moroteuthensis limits longevity or reproduction in common squids like C. calyx, then changes in parasite abundance might result in cascading effects on abundance of Chiroteuthis’ prey, predators and competitors. Additionally, baseline estimates of parasite prevalence are crucial to fully understand whether midwater host-parasite systems are at risk from increasing anthropogenic stressors and how they will change over time. While ROV observations have proven key to estimate prevalence and infection intensity here, trawled specimens continue to be valuable for verification of parasite species and obtaining material for genetic analyses, even if slightly damaged. We therefore recommend combining ROV observations with periodic trawling in future studies, since ROVs may not reveal smaller parasites, early infections or parasites in animals with tissue that is not transparent. More

Australia’s tropical rainforests are some of the oldest in the world.Credit: Alexander Schenkin

The rate of tree dying in the old-growth tropical forests of northern Australia each year has doubled since the 1980s, and researchers say climate change is probably to blame.The findings, published today in Nature1, come from an extraordinary record of tree deaths catalogued at 24 sites in the tropical forests of northern Queensland over the past 49 years.“Trees are such long-living organisms that it really requires huge amounts of data to be able to detect changes in such rare events as the death of a tree,” says lead author David Bauman, a plant ecologist at the University of Oxford, UK. The sites were initially surveyed every two years, then every three to four years, he explains, and the analysis focused on 81 key species.Bauman and his team recorded that 2,305 of these trees have died since 1971. But they calculated that, from the mid-1980s, tree mortality risk increased from an average of 1% a year to 2% a year (See ‘Increasing death rate’).

Bauman says that trees help to slow global warming because they absorb carbon dioxide, so an increase in tree deaths reduces forests’ carbon-capturing ability. “Tropical forests are critical to climate change, but they’re also very vulnerable to it,” he explains.Climate changeThe study found that the rise in death rate occurred at the same time as a long-term trend of increases in the atmospheric vapour pressure deficit, which is the difference between the amount of water vapour that the atmosphere can hold and the amount of water it does hold at a given time. The higher the deficit, the more water trees lose through their leaves. “If the evaporative demand at the leaf level can’t be matched by water absorption in fine roots, it can lead to leaves wilting, whole branches dying and, if the stress is sustained, to tree death,” Bauman says.The researchers looked at other climate-related trends — including rising temperatures and an estimate of drought stress in soils — but they found that the drying atmosphere had the strongest effect. “What we show is that this increase [in tree mortality risk] also closely followed the increase in atmospheric water stress, or the drying power of air, which is a consequence of the temperature increase due to climate change,” Bauman explains.Of the 81 tree species that the team studied, 70% showed an increase in mortality risk over the study period, including the Moreton Bay chestnut (Castanospermum australe), white aspen (Medicosma fareana) and satin sycamore (Ceratopetalum succirubrum).The authors also saw differences in mortality in the same tree species across plots, depending on how high the atmospheric vapour pressure deficit was in each plot.“This is one data set where the trees have been monitored in reasonably good detail since the early ’70s, and this is a really top-notch analysis of it,” says Belinda Medlyn, an ecosystem scientist at University of Western Sydney, Australia.But she says that more experiments are needed to determine whether the vapour pressure deficit is the biggest climate-related contributor to the increase in tree deaths. More

Young cerebrospinal fluid probably improves the conductivity of the neurons in ageing mice.Credit: Qilai Shen/Bloomberg/Getty

Young brain fluid improves memory in old miceCerebrospinal fluid (CSF) from young mice can improve memory function in older mice, researchers report in Nature (T. Iram et al. Nature 605, 509–515; 2022).A direct brain infusion of young CSF probably improves the conductivity of the neurons in ageing mice, which improves the process of making and recalling memories.CSF is a cocktail of essential ions and nutrients that cushions the brain and spinal cord and is essential for normal brain development. But as mammals age, CSF loses some of its punch. Those changes might affect cells related to memory, says co-author Tal Iram, a neuroscientist at Stanford University in California.The researchers found that young CSF helps ageing mice to generate more early-stage oligodendrocytes, cells in the brain that produce the insulating sheath around nerve projections and help to maintain brain function.The team suggest that the improvements are largely due to a specific protein in the fluid.“This is super exciting from the perspective of basic science, but also looking towards therapeutic applications,” says Maria Lehtinen, a neurobiologist at Boston Children’s Hospital in Massachusetts.Gender bias worms its way into parasite namingA study examining the names of nearly 3,000 species of parasitic worm discovered in the past 20 years reveals a markedly higher proportion named after male scientists than after female scientists — and a growing appetite for immortalizing friends and family members in scientific names.Robert Poulin, an ecological parasitologist at the University of Otago in Dunedin, New Zealand, and his colleagues combed through papers published between 2000 and 2020 that describe roughly 2,900 new species of parasitic worm (R. Poulin et al. Proc. R. Soc. B https://doi.org/htqn; 2022). The team found that well over 1,500 species were named after their host organism, where they were found or a prominent feature of their anatomy.

Source: R. Poulin et al. Proc. R. Soc. B https://doi.org/htqn (2022)

Many others were named after people, ranging from technical assistants to prominent politicians. But just 19% of the 596 species named after eminent scientists were named after women, a percentage that barely changed over the decades (see ‘Parasite name game’). Poulin and his colleagues also noticed an upward trend in the number of parasites named after friends, family members and even pets of the scientists who formally described them. This practice should be discouraged, Poulin argues.

Sea anemones turn oxybenzone into a light-activated agent that can bleach and kill corals.Credit: Georgette Douwma/Getty

Anemones suggest why sunscreen turns toxic in seaA common but controversial sunscreen ingredient that is thought to harm corals might do so because of a chemical reaction that causes it to damage cells in the presence of ultraviolet light.Researchers have discovered that sea anemones, which are similar to corals, make the sun-blocking molecule oxybenzone water-soluble by tacking a sugar onto it. This inadvertently turns oxybenzone into a molecule that — instead of blocking UV light — is activated by sunlight to produce free radicals that can bleach and kill corals. The animals “convert a sunscreen into something that’s essentially the opposite of a sunscreen”, says Djordje Vuckovic, an environmental engineer at Stanford University in California.It’s not clear how closely these laboratory-based studies mimic the reality of reef ecosystems. The concentration of oxybenzone at a coral reef can vary widely, depending on factors such as tourist activity and water conditions. And other factors threaten the health of coral reefs; these include climate change, ocean acidification, coastal pollution and overfishing. The study, published on 5 May (D. Vuckovic et al. Science 376, 644–648; 2022) does not show where oxybenzone ranks in the list. More

DataAll data was processed and analyzed using R (R Core Team, Version 4.0.3).Dengue case data were collected and shared by the Alcaldía de Medellín, Secretaría de Salud. In Medellin, dengue case surveillance is conducted by public health institutions that classify and report all cases that meet the WHO clinical dengue case criteria for a probable case to Medellin’s Secretaría de Salud through SIVIGILA (“el Sistema Nacional de Vigilancia en Salud Publica). All case data were de-identified and aggregated to the SIT Zone level.Human public transit usage and movement data were collected and shared by the Área Metropolitana del Valle de Aburrá for 50–200 respondents per SIT Zone. The “Encuestas Origen Destino” (Origen Destination Surveys) were conducted in 2005, 2011, and 2016 and published in 2006, 2012, and 2017, with survey methods described by the Área Metropolitana del Valle de Aburrá25. Survey respondents include a randomly selected subset of all Medellin residents in each SIT zone regardless of whether they use public transit or not. Survey respondents reported the start and end locations, purpose for travel, and mode of travel for all movement over the last 24 h from the time the survey was administered. Respondents reported all modes of movement, including public transit, private transit, and movement on foot. The results of the survey published in 2017 are published online by the Área Metropolitana del Valle de Aburrá26, and select data are available through the geodata-Medellin open data portal27. The results and data of the survey published in 2012 are not publicly available and were obtained directly from the Área Metropolitana del Valle de Aburrá.The public transit usage survey data were also used to extract socioeconomic data to the SIT zone; surveyors also reported basic demographic data including household Estrato, which was averaged per SIT zone to estimate zone socioeconomic status. “Estrato” measures socioeconomic status on a scale from 1 (lowest) to 6 (highest). This system is used by the government of Colombia to allocate public services and subsidies (Law 142, 1994). Data from the public transit usage survey were used to extract socioeconomic status data because it is the only location available where the spatial scale of the data matched the spatial scale of the SIT zone.Data on the location of Medellín public transit lines was downloaded as shape files from the geodata-Medellín open data portal27 and subset for each year to the set of transit lines that was available in that year. Data on the opening date of each Medellín public transit line was taken from the Medellín metro website28.Because census data at the zone level were not available for this study and only exists for 2005 and 2018, we used population estimates for each year downloaded from the WorldPop project29 and aggregated by SIT zone. The accuracy of WorldPop estimates were checked against available census data for 2005 and 2018 at the comuna level, accessed via the geodata- Medellín open data portal27.Ethical considerationsNo human subjects research was conducted. All data used was de-identified, and the analysis was conducted on a database of cases meeting the clinical criteria for dengue with no intervention or modification of biological, physical, psychological, or social variables. All methods were performed in accordance with the relevant guidelines and regulations.Data analysisQuantifying public transit usage and distance from nearest transit lineTo quantify public transit usage, we determined if each respondent reported using the metro, metroplus, or ruta alimentadora (supplementary bus route system integrated with the metro system) in the last 24 h. We then calculated the percent of respondents using the public transit system at least once for each SIT zone.To quantify the distance to the nearest public transit line, we calculated the distance from the center point of each zone to the closest metro, metroplus, tranvía, metrocable, ruta alimentadora, or escalera eléctrica. This was recalculated for each year, including new transit lines that were added within that year.Spatial autoregressive models of dengue incidenceDengue incidence per year at the level of the SIT zone was modeled using a fixed effects spatial panel model by maximum likelihood (R package splm30) as described in31. Our fixed effects were socioeconomic status, distance from public transit, a two-way interaction between these factors, and year. To weight dengue cases by population per SIT zone, the model contained a log offset of population per zone per year. Dengue case counts were log transformed after adding one to account for zones with zero dengue cases in a given year. Year was analyzed as a categorical variable to avoid smoothing epidemic years. All continuous variables were scaled to enable comparison of effect size. Because these panel models require balanced data across time, data was truncated to SIT zones that had data for all years available (247 remaining of 291). Spatial dependency was evaluated, and the model was selected using the Hausman specification test and locally robust panel Lagrange Multiplier tests for spatial dependence. Based on a significant Hausman specification test result, which indicates a poor specification of the random effect model, a fixed effect model was chosen. This result is supported by the fact that we had a nearly exhaustive sample of SIT zones in the Medellin metro area. Lagrange multiplier tests were used to determine the most appropriate spatial dependency specifications. Based on the results of the Lagrange multiplier tests, a Spatial Autoregressive (SAR) model was the most appropriate to incorporate spatial dependency; a SAR model considers that the number of dengue cases in a SIT zone depends on the number in neighboring zones.Because public transit usage was a measurement taken during just two of the study years, we constructed an additional fixed effects spatial panel model by maximum likelihood model of dengue incidence in just 2011 and 2016 that included ridership as an additional predictor variable. Our fixed effects were year, socioeconomic status, distance from public transit, a two-way interaction between socioeconomic status and distance from public transit, percent utilizing public transit, and a two-way interaction between socioeconomic status and percent utilizing public transit. As in our model of all years, the model contained a log offset of population per zone per year and dengue case counts were log transformed after adding one to account for zones with zero dengue cases in a given year, year was analyzed as a categorical variable, and all continuous variables were scaled to enable comparison of effect size. The data was truncated to SIT zones that had data for all years available (251 remaining of 291). We used the same model selection process, and again a fixed effect model was chosen, and based on the results of the Lagrange multiplier tests, a Spatial Autoregressive (SAR) model was determined the most appropriate to incorporate spatial dependency. More

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Study areaWith an area of about 2.33 × 105 km2, the Western Sichuan Plateau (27.11°–34.31°N and 97.36°–104.62°E) is located in the transition zone between the Qinghai-Tibet Plateau and the Sichuan Basin, including all of Garze Prefecture and Aba Prefecture, and parts of Liangshan Yi Autonomous Prefecture28 (Fig. 1). With an altitude of 780–7556 m, this area is dominated by mountain and ravine areas and high mountain and plateau areas, and the terrain is high in the west and low in the east. The climate belongs to the subtropical plateau monsoon climate, with large temperature difference between day and night and abundant sunshine. The annual average temperature is about 9.01–10.5 °C, and the precipitation is about 556.8–730 mm28. The study area is rich in water resources, including the Yalong River, Minjiang River and other important river systems in the upper reaches of the Yangtze River, and the Baihe River, Heihe river and other river systems of the Yellow River. The main types of soil are plateau meadow soil dark brown soil, brown soil, cold frozen soil and cinnamon soil, and the main vegetation types are alpine meadow and scrub. With rich and diverse soil vegetation types and distinctive vertical zonal distribution characteristics, it is one of the global biodiversity conservation hotspots29.Figure 1Location of the study area. The map is created in the support of ArcGIS 10.2 (ESRI). The China map and Western Sichuan Plateau boundary data were collected from Resources and Environmental Science and Data Center (http://www.resdc.cn/). The Qinghai-Tibetan Plateau boundary data were collected from the Global Change Research Data Publishing & Repository (http://www.geodoi.ac.cn/WebCn/Default.aspx).Full size imageData source and processingMultisource archival data were used in this study (Table 1). The land use remote sensing monitoring data, administrative boundary data and geological disaster vector data were obtained from Resources and Environmental Science and Data Center. The spatial resolution of land use remote sensing monitoring data is 30 × 30 m, including 6 first-level classification and 26s-level classification. The first-level classification includes cropland, woodland, grassland, water body, built-up land, and unused land. The accuracy of remote sensing classification is not less than 95% for cropland and built-up land, not less than 90% for grassland, woodland, and water body, and not less than 85% for unused land, which meets the need of the research. Landsat remote sensing monitoring data is used as the main information resources, among which Landsat-TM/ETM remote sensing monitoring data is used in 2000, 2005, 2010 and Landsat 8 remote sensing monitoring data is used in 2015 and 2020. In light of actual conditions and the implementation of policies and philosophies including the natural forest protection project, return of farmland to forest, land remediation, ecological civilization, the period from 2000 to 2020 is selected as the study period, and the land use data of each period is cropped using ArcGIS 10.2 to reclassify the 26 secondary classifications into cropland, woodland, grassland, water body, built-up land and unused land.Table 1 Characteristics of data used for the study.Full size tableThe DEM data were obtained from SRTM (Shuttle Radar Topography Mission) of Resources and Environmental Science and Data Center, the spatial resolution of 30 × 30 m, absolute horizontal accuracy ± 20 m, absolute elevation accuracy ± 16 m, elevation and slope are extracted from the downloaded DEM. The Qinghai-Tibetan Plateau boundary data were collected from the Global Change Research Data Publishing & Repository. Data of carbon density of different land types were obtained from Chinese Ecosystem Research Network Data Center (http://www.nesdc.org.cn/).A total of 29,284 evaluation units were collected for spatial grid processing of the Western Sichuan Plateau according to 3 km × 3 km by ArcGIS 10.2. The impact factors obtained in this study include grid data per kilometer of GDP spatial distribution, grid data per kilometer of population spatial distribution, annual mean temperature spatial interpolation data, annual mean rainfall spatial interpolation data, long-term normalized difference vegetation index (NDVI) comes from Resources and Environmental Science and Data Center with a resolution of 1 km × 1 km. The Human Active Index (HAI), with a resolution of 30 m × 30 m, can be calculated by formula30,31, and the factors are discretized into the data type required for the geodetector by the natural breakpoint method.MethodsThe InVEST modelThe InVEST model was developed by Stanford University, the University of Minnesota, the Nature Conservancy and the World Wide Fund for Nature (WWF). The model’s terrestrial ecosystem services assessment includes four modules: soil conservation, water retention, carbon storage and biodiversity assessment, and provides an overall measurement of regional ecosystem services32. The carbon storage model of the InVEST model divides the carbon storage of the ecosystem into 4 basic carbon pools, namely above-ground carbon, underground carbon, soil carbon, dead organic matter carbon7.The calculation formula of total carbon storage in the Western Sichuan Plateau is as follows7:$$C_{total} = C_{above} + C_{below} + C_{soil} + C_{dead}$$
(1)
In formula (1), Ctotal is the total carbon storage; Cabove is the above-ground carbon storage; Cbelow is the underground carbon storage; Csoil is the soil carbon storage, and Cdead is the dead organic matter carbon storage.Based on the carbon density and land use data of different land use type, the carbon storage of each land use type in the Western Sichuan Plateau is calculated by the formula7:$$C_{{text{total}}i} = (C_{{text{above}}i} + C_{{text{below}}i} + C_{{text{soil}}i} + C_{{text{dead}}i}) times A_{i}$$
(2)
In formula (2), i is the average carbon density of each land use, and Ai is the area of this land used.The carbon density data of different land use types in this study were obtained from the shared date of the National Ecological Science Data Center and some documents33,34,35,36,37. Since the carbon density data were collected from the results of studies in different parts of China, the selected documents should be close to or similar to the study area as far as possible to avoid excessive data gap. At the same time, the carbon density varies with climate, soil properties and land use38, so the carbon density should be modified according to the climate characteristics and land use types of the Western Sichuan Plateau. Existing research results show that the carbon density is positively correlated with annual precipitation and weakly correlated with annual average temperature. The quantitative expression of the relationship between carbon density and temperature and precipitation is as follows39,40,41,42:$$C_{SP} = 3.3968 times P + 3996.1;;left( {{text{R}}^{{2}} = 0.{11}} right)$$
(3)
$$C_{BP} = 6.7981e^{0.00541p};;;left( {{text{R}}^{{2}} = 0.{7}0} right)$$
(4)
$$C_{BT} = 28 times {text{T}} + 398;;left( {{text{R}}^{{2}} = 0.{47,};{text{P}} < 0.0{1}} right)$$ (5) In these formula, CSP is the soil carbon density (kg m−2) based on the annual precipitation; CBP is the biomass carbon density (kg m−2) based on the annual precipitation; CBT is the biomass carbon density (kg m−2) based on annual average temperature; P is the average annual precipitation (mm), and T is the annual average temperature (°C). According to the data of China Meteorological Data Service Centre (http://data.cma.cn/), in the past 20 years, the average annual temperature of China and the Western Sichuan Plateau was 9.0 °C and 6.3 °C, and the average annual precipitation was 643.50 mm and 812.65 mm respectively.The modified formula of carbon density in the Western Sichuan Plateau is as follows7:$$K_{BP} = frac{C^{prime}{_{BP}}}{{C^{primeprime}{_{BP}}}}$$ (6) $$K_{BT} = frac{C^{prime}{_{BT}}}{{C^{primeprime}{_{BT}}}}$$ (7) $$C_{BT} = 28 times T + 398;;left( {{text{R}}^{{2}} = 0.{47,};{text{P}} < 0.0{1}} right)$$ (8) $$K_{S} = frac{C^{prime}{_{SP}}}{{C^{primeprime}{_{SP}}}}$$ (9) In these formula, KBP is the modified indices of precipitation factor in biomass carbon density; KBT is the modified indices of temperature factor; C'BP and C''BP are the biomass carbon density obtained from annual precipitation in the Western Sichuan Plateau and the whole country respectively. C'BT and C''BT are the biomass carbon density obtained from annual average temperature; C'SP and C''SP are the soil carbon density data obtained from annual average temperature; KB and KS are the biomass carbon density modified indices and soil carbon density modified indices respectively. The carbon density values of each land use type after modified in the Western Sichuan Plateau are shown in Table 2.Table 2 Carbon density values of different land use types in the Western Sichuan plateau (t hm−2).Full size tableExploratory spatial analysis methodGlobal spatial autocorrelationGlobal Moran’s I was used to describe the spatial differentiation characteristics of carbon storage in the study area, and the expression formula is as follows43:$$I = frac{{nsumnolimits_{i = 1}^{n} {sumnolimits_{j = 1}^{n} {w_{i,j} left( {x_{i} - overline{x} } right)left( {x_{j} - overline{x} } right)} } }}{{sumnolimits_{i = 1}^{n} {sumnolimits_{j = 1}^{n} {omega_{ij} } } sumnolimits_{i = 1}^{n} {left( {x_{i} - overline{x} } right)^{2} } }}$$ (10) wij is the spatial weight; x is the attribute mean; xi and xj are the attribute values of elements i, j, respectively; n is the number of cells, and the correlation is considered significant when |Z|  > 1.96.Local indications of spatial association (LISA)LISA reveals the local cluster characteristics of spatial unit attributes by analyzing the difference and significance between spatial units and surrounding units, and the expression formula is as follows42:$$I_{i} (d) = frac{{n(x_{i} – overline{x} )sumnolimits_{j = 1}^{n} {w_{ij} (x_{j} – overline{x} )} }}{{sumnolimits_{i = 1}^{n} {(x_{j} – overline{x} )^{2} } }}$$
(11)
Correlation analysisIn order to evaluate the influence of natural factors and socioeconomic factors on carbon storage in the study area, the correlation coefficients of temperature, rainfall, NDVI, GDP, population density (PD), HAI and carbon storage were calculated according to the Pearson correlation coefficient method. The calculation formula is as follows44:$$r_{xy} = frac{{sumnolimits_{i = 1}^{n} {(M_{i} – overline{x} )(y_{i} – overline{y} )} }}{{sqrt {sumnolimits_{i = 1}^{n} {(M_{i} – overline{x} )^{2} sumnolimits_{i = 1}^{n} {(y_{i} – overline{y} )} } } }}$$
(12)
rxy represents the correlation coefficient between x and y; Mi represents the carbon storage in the ith year; yi represents the value of the impact factor Y in the ith year, and ({overline{text{x}}}) and ({overline{text{y}}}) respectively represents the average value of carbon storage and impact factor in the research period over several years.Human influence index analysis methodLand use is significantly spatially clustered in the study area31, and LUCC changes will have a certain impact on the structure and process of the ecosystem. HAI has the characteristics of spatial variability, which can reflect the impact of human activities on land use and landscape composition changes. In this study, Human Influence Index Analysis Method (HAI) index was used to analyze the correlation between carbon storage and human interference intensity in the Western Sichuan Plateau. The calculation formula is as follows30,$$HAI = sumlimits_{i = 1}^{n} {left( {A_{i} P_{i} /TA} right)}$$
(13)
HAI is Human Active Index; Ai is the total area of the ith land use type; Pi The intensity parameter of human impact reflected by type i land use type; TA is the total final surface area of land use type in evaluation unit; n is the number of land use types. Combined with the land use type of this study, Pi is assigned by Delphi method, in which cropland is 0.67, woodland is 0.13, grassland is 0.12, water body is 0.10, built-up land is 0.96, and unused land is 0.0530,45.GeodetectorGeodetector is an algorithm that uses spatial heterogeneity principle to detect driving factors of carbon storage, which can quantitatively detect the influence of impact factors on carbon storage and explore the interaction between driving factors. Geodetector includes factor detection, risk detection, interaction detection and ecological detection46.Differentiation and factor detection: the influence factors were discretized, and then the significance test of the difference in the mean values of the impact factors was conducted to detect the relative importance among the factors. The statistical quantity q is used to measure the explanatory power of impact factors on the carbon storage spatial differentiation and the value range of q is between 0 and 1. The larger the value, the stronger the explanatory power of the factor47.$$q = 1{ – }frac{{sumnolimits_{h = 1}^{L} {N_{h} sigma_{h}^{2} } }}{{Nsigma^{2} }}$$
(14)
In this formula, h = 1, 2…, L is the classification or partition of variable (Y) or factor (X); Nh and N are layer h and regional number units respectively; and (sigma_{h}^{2}) and (sigma_{{}}^{2}) are the variance of the layer h and regional value Y respectively.The variance of the regional value Y is calculated as follows,$$sigma^{2} = frac{{sumnolimits_{i = 1}^{n} {(Y_{i} – overline{Y} )^{2} } }}{N – 1}$$
(15)
where, Yi and (overline{Y}) are the mean value of sample j and the region Y, respectively.$$sigma^{2} = frac{{sumnolimits_{i = 1}^{{n_{h} }} {(Y_{h,i} – overline{{Y_{h} }} )^{2} } }}{{N_{h} – 1}}$$
(16)
where, Y and (overline{Y}) are the value and mean value of sample i in layer h, respectively.Interaction detection: it is used to identify the interaction between different impact factors Xs, that is, to evaluate whether the combined action of X1 and X2 will increase or weaken the explanatory power of vegetation coverage Y, or the influence of these factors on Y is independent of each other. The evaluation method is to first calculate the value q of the two factors X1 and X2 for Y respectively: q(X1) and q(X2), and calculate the value q of their interaction (the new polygon distribution formed by the tangent of the two layers of the superimposed variables X1 and X2) : q(X1 ∩ X2) and compare q(X1) and q(X2) with q(X1 ∩ X2)46. More

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