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Climate change is affecting arctic ecosystems through temperature increase1, hydrological changes2, earlier snowmelt3,4, and the associated increase in growing season length5. Annual arctic air temperature has been increasing at more than double the magnitude of the global mean air temperature increase1, and terrestrial snow cover in June has decreased by 15.2% per decade from 1981 to 20194. Warming is the main driver of the earlier start of the growing season, and the greening of the Arctic6,7,8. Arctic greening is associated with enhanced vegetation height, biomass, cover, and abundance9. However, the complexity of arctic systems reveals an intricate patchwork of landscape greening and browning8,10,11, with browning linked to a variety of stresses to vegetation8 including water stress12,13. The interconnected changes in temperature, soil moisture, snowmelt timing, etc. can have important effects on the carbon sequestered by arctic ecosystems14. The reservoir of carbon in arctic soil and vegetation depends on the interaction of two main processes: (1) changes in net CO2 uptake by vegetation; and (2) increased net loss of CO2 (from vegetation and soil) to the atmosphere via respiration. Therefore, defining the response of both plant productivity and ecosystem respiration to environmental changes is needed to predict the sensitivity of the net CO2 fluxes of arctic systems to climate change.An earlier snowmelt, and a longer growing season, do not necessarily translate into more carbon sequestered by high latitude ecosystems5. There is a large disagreement on the response of plant productivity and the net CO2 uptake to early snowmelt in tundra ecosystems15,16,17,18,19. A warmer and longer growing season might not result in more net CO2 uptake if CO2 loss from respiration increases16, particularly later in the season, and surpasses the CO2 sequestered by enhanced plant productivity in northern ecosystems16,20. Moreover, snowmelt timing and the growing season length greatly affect hydrologic conditions of arctic soils21, as well as plant productivity22. Longer non-frozen periods earlier in the year23 and earlier vegetation greening can increase evapotranspiration (ET), resulting in lower summer soil moisture24,25,26. The complexity in the hydrology of tundra systems arises from the tight link between the water drainage and the presence and depth of permafrost. The presence of permafrost reduces vertical water losses, preventing soil drainage in northern wetlands during most of the summer despite low precipitation input27. Increasing rainfall28 and increased permafrost degradation can increase soil wetness in continuous permafrost regions2. Further permafrost degradation (e.g. ice-wedge melting) can increase hydrologic connectivity leading to increased lateral drainage of the landscape and subsequent soil drying2,29.Given the importance of soil moisture in affecting the carbon balance of arctic ecosystems, and its links with snowmelt timing, in this study, we investigated the correlation between summer fluxes of CO2 (i.e., net ecosystem exchange (NEE), gross primary productivity (GPP) ecosystem respiration (ER)), ET, and environmental drivers such as soil moisture and snowmelt timing, while controlling for the other most important drivers of photosynthesis and respiration (i.e. solar radiation and air temperature). We expected earlier snowmelt to be correlated with larger ET and lower soil moisture, particularly during peak and late season, consistent with drying associated with a longer growing season. The lower soil moisture with earlier snowmelt should result in a negative correlation between snowmelt timing and GPP, particularly during the peak and late season (when we expect the most water stress), and in a positive correlation between snowmelt timing and ER during the entire growing season. This soil moisture limitation to plant productivity should result in lower net cumulative CO2 sequestration during the entire summer, because of lower plant productivity if these ecosystems are water-limited due to lower soil moisture with earlier snowmelt.Testing the impact of snowmelt timing on the carbon dynamics and hydrology of tundra ecosystemsThe 11 sites were selected as among the longest-running tower sites in the circumpolar Arctic (including 6 to 19 years of fluxes per site and a total of 119 site-years of summer (June to August) eddy covariance CO2 flux data, Table S1). All sites lie in the zone of continuous permafrost. The sites are representative of dominant tundra vegetation classes (wetland, graminoids, and shrub tundra), together accounting for 31% of all tundra vegetation types (Fig. 130 and Supplementary Information). Given the complex interactions among different variables (many covarying together), we used a variety of statistical analyses to identify the association between standardized anomalies of NEE, GPP, ER, and ET, and standardized anomalies of the main environmental controls during different periods of the summer corresponding to various stages in seasonal phenology (early season: June, peak season: July, and late season: August). We used a partial correlation analysis to identify if the timing of the snowmelt associates with anomalies of ET, soil moisture, NEE, GPP, ER, atmospheric vapor pressure deficit (VPD), or the Bowen ratio (the ratio between Sensible Heat (H) and Latent Heat (LE)) while statistically controlling for the main meteorological forcing such as air temperature and solar radiation (Methods). Identifying the correlation between ET (and the Bowen ratio) and snowmelt timing is a way to assess water limitation to ecosystems (in addition to testing their response to soil moisture changes), as H, and therefore, the Bowen ratio, are expected to increase with surface drying31,32. To identify the association between snowmelt timing, the main environmental variables (i.e., air temperature and solar radiation), and NEE, GPP, ER, and ET over time, we performed a maximum covariance analysis (MCA) on the monthly median standardized anomalies from 2004 to 2019 (a time period when data for most of the sites were available). MCA allowed us to find patterns in two space–time datasets that are highly correlated using a cross-covariance matrix26. We retained sites as the unit of variation (i.e., by estimating the standardized anomalies by site for each of the indicated variables, see “Methods”), hence the results of the MCA integrated the site level relationships between each of the variables over time). The goal of this analysis was to identify the most important environmental drivers associated with NEE, GPP, and ER across all the sites over time. MCA is particularly appropriate for this study as it can handle data with gaps and unequal lengths in the datasets. We also tested the relative importance of the abovementioned environmental drivers on the monthly median GPP, ER, and NEE using a linear mixed effect model, including site as a random effect to account for the site-to-site variability. The MCA and the mixed model analyses were conducted to test the relative importance of snowmelt and other variables at different times of the season. Finally, to evaluate the water balance at different times of the season, we estimated the difference between Potential Evapotranspiration (PET) and the actual ET, and the difference between precipitation (PPT) and ET for each of the sites, years, and months (e.g. June, July, and August). This study did not attempt to describe the long-term temporal changes in the anomalies of snowmelt and carbon fluxes, given the short data record available for some of the sites (i.e. less than 10 years, Table S1), but instead focused on understanding the association between environmental variables and the carbon balance at different times of the season. More details of these analyses are included in the Methods.Figure 1Study sites. Locations of the 11 eddy covariance flux tower sites used in this study. Light blue regions delineate the total Circumpolar Arctic Vegetation Map (CAVM), green regions delineate the subset of CAVM vegetation types represented in this study (including all the vegetation types listed in Table S1). This map was created using QGIS.org, 2020, QGIS 3.10. Geographic Information System User Guide. QGIS Association: https://docs.qgis.org/3.10/en/docs/user_manual/index.html. The dataset used in the map was the CAVM map: CAVM Team. 2003. Circumpolar Arctic Vegetation Map. (1:7,500,000 scale), Conservation of Arctic Flora and Fauna (CAFF) Map No. 1. U.S. Fish and Wildlife Service, Anchorage, Alaska. ISBN: 0-9767525-0-6, ISBN-13: 978-0-9767525-0-9.Full size imageInfluence of snowmelt timing on NEE, GPP, ER, and hydrological status of tundra ecosystemsOnce statistically controlling for solar radiation and air temperature (in the partial correlation analysis, see “Methods”), we observed a significant positive relationship between the snowmelt timing anomalies and NEE anomalies (i.e. earlier snowmelt was associated with a higher net CO2 sequestration) in June and July, but a negative correlation in August (Fig. 2a, Table 1). A significant relationship was also found between snowmelt date anomalies and GPP anomalies, with more positive GPP anomalies (i.e. higher plant productivity) with earlier snowmelt in June and July, and more negative GPP anomalies with earlier snowmelt in August (Fig. 2b, Table 1). Earlier snowmelt was associated with significantly higher ER in both June and July, but there was no significant relationship in August (Fig. 2c, Table 1), suggesting that the late-season correlation between NEE and snowmelt timing was mostly driven by the lower GPP and with earlier snowmelt in August. The MCA analysis showed that the anomalies in snowmelt timing had the highest squared covariance fraction (SCF) with the monthly median anomalies of GPP, NEE, and ER in June and July, and the lowest in August over the 2004–2019 period (Fig. 3). A similar result was observed in the linear mixed effect model, which showed a significant relationship between snowmelt date and GPP, and NEE, in all summer months, higher ({R}_{m}^{2}) between the snowmelt date and GPP in June and July, and no significant relationship between snowmelt date and ER in August (Table S3). In late season, other environmental variables had a higher covariance with the GPP, NEE, and ER anomalies than the snowmelt timing (Fig. 3, Table S3).Figure 2Relationships between the indicated median monthly anomalies using partial correlation analysis accounting for solar radiation and air temperature anomalies (retaining site as the unit of variation). Given that the interaction term between “month” and snowmelt timing was significant, we included the correlation coefficients and P of the regressions for each of the indicated months separately in each panel (also included in Table 1). Negative values indicate CO2 uptake and positive values CO2 release into the atmosphere, and shaded areas are 95% confidence intervals.Full size imageTable 1 Significance (P) and Pearson’s correlation coefficient (r) of the relationships between the indicated monthly median standardized anomalies for June, July, and August retaining site as a unit of variation.Full size tableFigure 3Squared covariance fraction (SCF) of each couple of the indicated variables for the maximum covariance analysis (MCA) of the monthly median anomalies of GPP, ER, and NEE in June, July, and August. The first pair of singular vectors are the phase-space directions when projected that have the largest possible cross-covariance. The singular vectors describe the patterns in the anomalies that are linearly correlated. A higher SCF indicates a stronger association over time between the indicated variables.Full size imageOur results are consistent with the discrepancy between the observed increase in the maxNDVI over the last four decades and the time-integrated (TI) NDVI which instead has plateaued in the last two decades and even decreased over the last 10 years in several northern arctic ecosystems33. TI-NDVI considers the length of the growing season and phenological variations34 and, therefore, better integrates vegetation development during the entire growing season. Moisture was shown to be important for the NDVI trends33,35. Given the potential water limitation to summer carbon uptake in northern ecosystems12,23,24,25, we tested if an earlier snowmelt was associated with a decrease in soil moisture, which would affect GPP and NEE. We only observed a significant correlation between soil moisture anomalies and snowmelt date anomalies in June (i.e. higher soil moisture with earlier snowmelt, Fig. S1a, Table S2), but no significant correlation in July and August (Fig. S1a, Table S2). The higher soil moisture with earlier snowmelt in June is consistent with surface inundation after snowmelt36,37 and earlier soil thawing resulting in higher soil moisture (i.e., soil moisture is low while soils are frozen). A similar result was observed for the ET anomalies. Higher ET with earlier snowmelt in June (Fig. S1b) could be the result of surface inundation after snowmelt32. The standardized NEE anomalies were significantly correlated with the soil moisture anomalies in each of the summer months (Fig. S1d, Table S2). However, the relationship between the GPP (and ER anomalies) and soil moisture anomalies was only significant in June (Fig. S1e,f, Table S2) suggesting an earlier activation of the vegetation with earlier soil thaw (and the associated higher soil moisture). A higher water loss from ET in early season (Fig. S1b) could have resulted in the drying of the surface moss layer with the progression of the summer, which would have been consistent with the observed lower GPP and the lower net CO2 sequestration with earlier snowmelt observed in August (Fig. 2a,b, Table 1). A potential moisture limitation to plant productivity might have been consistent also with the higher SCF of NEE, or GPP and VPD anomalies in August than in June and July (Fig. 3). However, no significant relationship between ET (or soil moisture) and snowmelt date anomalies was observed in July and August (Fig. S1a,b) contrary to what would be expected if drying occurred following earlier snowmelt. No significant relationship was found between VPD anomalies and snowmelt date anomalies in any of the summer months (P = 0.14 in a partial correlation considering air temperature and solar radiation anomalies). Finally, surface drying should result in an increase in the Bowen ratio anomalies with the progression of the summer, given that H increases with a decrease in water table and surface drying32,38. However, the Bowen ratio showed no correlation with the standardized snowmelt date anomalies in any of the summer months (Fig. S1c, Table S2), and presented similar values in all the summer months (Fig. S2a). The lack of correlation between the soil moisture, VPD, Bowen ratio, and snowmelt date anomalies suggests that an earlier snowmelt did not result in significant surface drying in the sites of this study. The median PET-ET and PPT-ET for all years and sites included in this analysis (Fig.S2 b,c) was slightly higher in August, similar to reports by others for the Russian arctic tundra38,39, further supporting a lack of soil moisture limitation in late season. Although these analyses do not consider runoff, which can be significant21,26, overall our results do not suggest that an earlier snowmelt resulted in a water stress that significantly limited plant productivity in these arctic ecosystems over continuous permafrost.The correlation between the anomalies in the August GPP and snowmelt timing is consistent with earlier senescence in northern plant species (e.g. Eriophorum vaginatum, a dominant species across these tundra types) compared to southern species growing in the same location in a common garden experiment40. The phenotypic variation was shown to be persisting for decades41, and ecotypes may be unable to extend their effective growth period or take advantage of a longer growing season40. Several studies across different plant functional types have shown that once plant growth is initiated after the snowmelt in northern ecosystems, it continues only for a fixed number of days until the occurrence of senescence42,43,44. Therefore, the lower GPP in August with earlier snowmelt might not be linked to water limitation on photosynthesis later in the season, but rather to an earlier senescence arising from the endogenous rhythms of growth and senescence, that plant functional types living in these extreme conditions have developed over decades. On a broader scale, earlier senescence with an earlier start of the growing season after snowmelt in northern ecosystems is also consistent with an earlier spring zero-crossing date and an earlier autumn zero-crossing date of the mean detrended seasonal CO2 variations at Barrow, AK, USA (NOAA ESRL: https://www.esrl.noaa.gov/gmd/ccgg/obspack/) during 2013–2017 compared to 1980–19845. The spring and autumn zero-crossing date is the time when the detrended seasonal CO2 variations intersect the zero line in spring and autumn respectively and can be used as an indicator for the start and end of the net CO2 uptake by vegetation45,46. On the other hand, NDVI measurements show both an earlier start of the season and a later end of season for 2008–2012 compared to 1982–19865. The disagreement between the detrended seasonal atmospheric CO2 concentration showing an earlier autumn zero-crossing date and the NDVI measurements showing a later end of the season has been explained by the increase in respiration in the fall20. The disagreement between atmospheric CO2 concentration trends (showing an earlier autumn zero-crossing date), and NDVI (showing a later end of the season,5 may also be explained by the challenges in using NDVI as a proxy for plant productivity in these arctic systems. The relationship between NDVI and CO2 flux and plant productivity is highly variable and non-linear in arctic ecosystems47. While some arctic ecosystems have shown that NDVI was strongly correlated with GPP (explaining 75% of the variation in GPP48, other studies showed that NDVI was either not significantly correlated with GPP and NEE49 or was only able to explain a minor fraction (maximum of 25%) of the variation in NEE and GPP in some arctic tundra ecosystems after accounting for the seasonal variation50,51.In conclusion, earlier snowmelt was associated with greater net CO2 uptake and higher GPP in early and peak seasons, but with less net CO2 uptake and lower GPP later in the summer, in the studied arctic tundra ecosystems. We did not find evidence of a late-season water limitation to GPP with earlier snowmelt. Although several hypotheses can be forwarded to explain the link between snowmelt and late season declines in plant productivity and carbon uptake, the current literature does not provide a definitive explanation (schematic Fig. 4). Future studies should investigate the potential interaction of different processes explaining the response of the carbon dynamics in the Arctic to earlier snowmelt and reconstruct the temporal changes in the carbon balance from these systems. The link between the long-term changes in the CO2 fluxes and NDVI in tundra ecosystems needs closer examination. Studies should investigate if higher NDVI is definitively associated with higher net CO2 uptake. Greening of the Arctic might not necessarily translate into more net CO2 uptake, as early and peak season carbon gains might be offset by a late-season CO2 loss, and respiration might counterbalance the increase in plant productivity. A better understanding of the processes driving these temporal changes is a fundamental step in advancing our prediction of the response of the arctic CO2 balance to changing climate.Figure 4Schematic of the effect of earlier snowmelt on NEE, GPP, and ER at different times of the season. Earlier snowmelt results in an earlier activation of the vegetation, higher plant productivity, and higher net carbon uptake in June and July. This earlier activation could result in more carbon loss and lower plant productivity with earlier snowmelt in August, potentially related to either environmental stress, or to earlier senescence. Photo credit: Donatella Zona.Full size image More

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A combined DNA metabarcoding/in-situ macro photography approach to reliably analyse carnivorous plant prey spectraResults indicate that DNA metabarcoding allows for reliable analysis of prey spectra composition in carnivorous plants at a taxonomic resolution and level of completeness unachievable by traditional morphology-based approaches (as performed, for example, by4,5,6,7,9,10,11). Even in remote tropical northern Western Australia, where many (if not most) arthropod species have not yet been accessioned into the BOLD or GenBank barcode reference libraries, this method identified over 90% of obtained OTUs from our sample set; most of them at family-level, but 41% to genus-level, and 17% even down to species rank (Supplementary Data S1). Lekesyte et al.27 were able to identify 80% of the analysed prey items found on D. rotundifolia in England to species-level. However, their sampling was performed in western Europe, whose entomofauna is comparatively well studied taxonomically and has an excellent coverage in the BOLD reference library of DNA barcodes41. New insect barcodes are regularly added to the BOLD library through large-scale initiatives such as the international Barcode of Life Project (iBOL; https://ibol.org/) and its Australian node Australian Barcode of Life Network (ABOLN), hence accuracy of future metabarcoding research performed in Australia can be expected to increase to similar levels soon.In-situ macro photography was found to provide a valuable plausibility control tool for the prey taxa identified by metabarcoding. While many of the smaller prey taxa detected by metabarcoding were impossible to identify in the in-situ macro photographs due to their tendency to quickly degenerate after digestion into small, shapeless “crumbs”8, this control method considerably reduced the amount of prey taxa detected which were not actually present as prey in the Drosera samples. This flaw of metabarcoding is most commonly a consequence of procedural errors resulting in cross-contamination within the DNA extraction procedure27, usually resulting in low read numbers. However, in-situ macro photographs may also fail to detect species if prey captured by the sundew escaped from the trap33,42, or was stolen by larger animals. In both cases, a DNA imprint left on the Drosera leaves as excretions, detached scales, hairs or, frequently, as autotomised (shedded) body parts42 could have been detected by metabarcoding. Additionally, some barcoding-detected taxa may not constitute prey if they were associated with another captured prey taxon (either as part of its diet, or as a parasite). The latter may explain some barcode hits for taxa not immediately apparent from the in-situ macro photographs, as they are (endo)parasites of captured prey taxa. This was likely the case in the detected Strepsiptera (stylops) which are frequently contained as larvae and adult females in their hymenopteran and orthopteran hosts43. However, insect endoparasites and other non-obvious prey taxa were by default not excluded by the very conservative approach of pictorial plausibility control. Additionally, in the case of endoparasites, these organisms would also contribute to plant nutrition as “bycatch” after being digested together with their host, despite not having been actively attracted to the carnivorous traps. Finally, the control method tested in this study showed that even heavily digested prey items in the samples had sufficient amounts of intact (mitochondrial) DNA present to be detected by metabarcoding, as we found no instance of any prey item being clearly identifiable in the macro photographs but not present in the barcoding data.Prey spectra composition of the studied Drosera speciesThe analysed prey spectra of the three studied species from D. sect. Arachnopus most commonly contained flying insects (especially of the orders Diptera and Hemiptera, both present in 100% of the samples; Fig. 3), thus confirming earlier in-situ macro photography-based studies of closely-related D. sect. Arachnopus species by Krueger et al.8. All members of D. sect. Arachnopus are characterised by a large, erect growth habit and thread-like aerial leaves which usually do not contact the ground8,32, thereby excluding most ground-dwelling arthropods as prey. This result is also similar to other prey spectra studies of erect-leaved Drosera from different geographic areas, where flying insects (particularly Diptera) unanimously comprised almost the entire recorded prey5,11,44. Furthermore, this study confirmed the result of Krueger et al.8 that Hemiptera—and within this order especially the Cicadellidae—are exceptionally common in the prey spectra of D. sect. Arachnopus compared with all other, previously studied Drosera. A possible explanation for this may be the relatively high abundance of Cicadellidae in tropical habitats45 compared to subtropical or temperate habitats where the above-mentioned previous Drosera prey spectra studies were conducted.Of the five most commonly detected orders, Lepidoptera generally comprised the largest prey items in terms of body size or wingspan, respectively. This prey order was exceptionally common in D. finlaysoniana, being present in 100% of samples and also visually conspicuous in the in-situ photographs. Since this Drosera species had by far the largest trapping leaves among the three species studied with an average leaf length of 10.4 ± 0.6 cm (Suppl Appendix S7), and exhibits the largest leaves in D. section Arachnopus32, this may represent an example of large prey items being more easily captured by species with larger trapping leaves33. Additionally, the sampled population of D. finlaysoniana was huge and dense (see Supplementary Figure S1), probably attracting larger prey and enabling capture of larger prey items by “collective” trapping46. Alternatively, Fleischmann30 suggested that captured Lepidoptera themselves could attract further individuals of the same species by pheromone release, potentially explaining the very high numbers of this insect order observed in D. finlaysoniana.Differences among observed prey spectraComparison of prey spectra between the three studied Drosera species revealed significant differences at arthropod family-level but not at the higher level of arthropod orders, indicating that at a coarse taxonomic resolution, the same five arthropod orders (Diptera, Hemiptera, Hymenoptera, Lepidoptera and Thysanoptera) generally comprise most of the prey in D. sect. Arachnopus, regardless of given Drosera species or habitat. However, as strong differences were discovered in the ANOSIM comparison at family-level, it can be concluded that differences might likely increase with finer taxonomic resolution of prey taxa, a conclusion also reached by the carnivorous plant prey spectra meta-analysis of Ellison & Gotelli47. While these differences may be partially attributed to different morphological traits of the three species such as leaf scent8,30 or eglandular appendages31, the very high ANOSIM R-values returned and the large number of prey families contributing nearly equally to dissimilarity (Table 2) indicate that the most likely explanation is very different available prey spectra at the three study sites. Indeed, significant differences among different study sites, even within the same species, were previously reported for Drosera rotundifolia by Lekesyte et al.27 and for four species from D. sect. Arachnopus by Krueger et al.8. Notably, the three study sites feature different habitat types and climate regimes (Supplementary Fig. S1).Analyses indicate that there is likely little specialisation in prey capture by the three studied Drosera species. For example, the relatively high detection rate of Lepidoptera in the samples of D. finlaysoniana and D. hartmeyerorum compared to D. margaritacea may be explained by the lake margin habitats of the former two species, while the latter species was found in a completely dry drainage channel lacking any nearby waterbodies (Supplementary Fig. S1). Lepidoptera are likely to occur in much higher concentrations near water sources, especially during the dry season (May to November) when the surrounding areas are lacking other water sources (G. Bourke in Fleischmann30).Estimating prey quantityIn addition to providing a plausibility control for the compositional prey analysis by metabarcoding, the in-situ macro photography method facilitated an estimation of prey quantity per sample. Metabarcoding by itself is currently not a reliable tool for prey quantification due to the lack of a linear relationship between the number of sequence reads and organism biomass26,27.In contrast to Krueger et al.8, who generally found more prey items on larger trapping leaves in species of D. sect. Arachnopus (even when values were compared as per cm of trapping leaf length), the species with the largest leaves studied here (D. finlaysoniana) captured significantly less prey items than the smaller-leaved species D. margaritacea and D. hartmeyerorum (Fig. 4). However, while Krueger et al.8 was able to compare sympatric species (thus minimising any potential effects of the habitat or region on prey spectra), the three species in this study were studied at three different, geographically distant sites. While it is possible that overall prey abundance in the habitat was much lower at the D. finlaysoniana study site (Site 1), it can be hypothesised that the low total prey capture observed in this species may be due to the very large and extremely dense population resulting in strong intraspecific competition for prey (see Supplementary Fig. S1). This effect of population structure on prey capture has also been observed by Gibson48 and Tagawa and Watanabe46 who found a significant negative correlation between total prey capture and population density in different species of Drosera.Conclusions and outlookOur study is the first to employ a DNA metabarcoding approach supported by controls for species presence to analyse carnivorous plant prey spectra. When combined with in-situ macro photography, this method is clearly superior in terms of taxonomic resolution and completeness for analysis of environmental bulk samples (containing different organisms in highly variable states of preservation), as used here for the reconstruction of prey spectra of carnivorous plants. The capability of this method increases with new reference barcodes being regularly added to DNA barcode libraries (such as BOLD and NCBI GenBank) and it thus has the potential to become the standard methodology for future carnivorous plant prey spectra research.Additional studies are needed to test this method for other carnivorous plant species and genera, especially those possessing different trap types. Within Western Australia, three additional trap types occur: snap traps (Aldrovanda), suction traps (Utricularia) and pitfall traps (Cephalotus). In particular, it might be expected that in-situ macro photography will not work as well for the extremely small, typically submerged traps of Aldrovanda and Utricularia (which also completely enclose their captured, microscopic prey items49), potentially necessitating usage of alternative control methods for metabarcoding data. Furthermore, even within Drosera (adhesive traps) some species may require adjustments to the methodology presented here as they accumulate captured prey in a central point via tentacle movement (e.g., many climbing tuberous Drosera) or their leaves may be very difficult to place on paper sheets with the sticky side facing upwards (e.g., all pygmy Drosera). The latter problem may be solved by using reverse action forceps and photographing the leaves while held in place by the forceps.Extensive sampling of sites with co-occurring species from D. sect. Arachnopus is clearly required to better understand the ecological role of trap scent and eglandular appendages in this section. For example, manipulation experiments involving the removal of all yellow blackberry-shaped appendages of D. hartmeyerorum (which have been hypothesised to function as visual prey attractants31) and subsequent metabarcoding prey spectra comparisons of mutilated plants lacking emergences with control plants are proposed. Potential effects of population density on prey spectra (as hypothesised here for D. finlaysoniana) could be studied by comparing prey spectra of individual plants from within mass populations with more exposed-growing individuals of the same population. More

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Concentration of TPHs in surface soilsStatistical results of TPHs concentrations at different geographic oilfields were showed in Fig. 2, and grid regional distribution of TPHs in YC Oilfield surface soils (Y6–Y25) were shown in Fig. 3. Results are given as mean value of triplicate analysis of each sample. The results of TPHs concentration in soil samples showed that the three oilfields all suffered from varying degrees of petroleum pollution, and 60.92% of the 47 sampling points was significantly higher than the soil critical value (500 mg/kg). The average concentration of the TPHs in each study areas conformed to be in the following law: SL Oilfield (average: 5.36 × 103 mg/kg) ( >) NY Oilfield (average: 1.73 × 103 mg/kg) ( >) YC Oilfield (average: 1.37 × 103 mg/kg). The highest concentration of the TPHs were found in SL Oilfield surface soils, ranging from 1.21 × 102 to 6.66 × 104 mg/kg, and NY Oilfield had the second highest TPHs concentrations in the range from 15.82 to 7.42 × 103 mg/kg. The concentrations of TPHs in YC Oilfield ranged from 12.34 to 5.38 × 103 mg/kg. The petroleum contamination mainly derived from abandoned and working oil wells. S4 and S8 soils were collected near the abandoned oil well and working oil well, respectively, and had the highest concentration of TPHs up to 5.28 × 104 and 6.66 × 104 mg/kg. Y1, N8 near the abandoned oil well also had high concentration of TPHs with 5.39 × 103 and 7.42 × 103 mg/kg, respectively. Pollution caused by grounded crude oil in exploitation process has been a serious problem in oilfield area. Our previous research reported that the TPHs content in Dagang Oilfield soils collected adjacent to working oil wells were about 20-folds higher than that in corn soils and living area soils25. Concentration contour map of TPHs in YC Oilfield by grid sampling method showed that regional pollution in the northwest and southeast area are more serious than other sites. Y6 near the gas station and Y15, Y21, Y23 adjacent to the working oil wells have higher concentration (2.12 × 103–5.34 × 103 mg/kg) of TPHs than other farmland and grass soils. Previous study reported that the concentrations of TPHs ranged 7.0 × 102–4.0 × 103 mg/kg in oil exploitation areas of the loess plateau region (34°20′N,107°10′E), showing a similar pollution level with this study26.Figure 2The concentration of TPHs in three oilfield soils.Full size imageFigure 3Grid regional distribution of TPHs in YC Oilfield.Full size imageThe percentage composition of total PAHs, SHs and polar components of petroleum hydrocarbons were shown in Table 1. In general, the dominant petroleum component was saturated hydrocarbons in all soils, accounting more than 50%. Yet, the percentage proportion of PAHs and SHs in contamination soils adjacent to working and abandon oil wells were significantly different (p  BbF (14.16–21.87%) ≫ BaA, Chr, InP, and BkF (less than 10%). This result aligned to the previous study that the contribution of individual PAHs to the TEQs of ∑PAH16 was BaP (45%)  > DBA (33%) in urban surface dust of Xi’an city, China46. Therefore, contamination control should priority focus on the individual PAHs of BaP, DBA, BbF in these areas. In addition, the ecological risk with abandoned time ranging 0–15 years has been assessed, and the descriptive statistic TEQBap of PAHs was shown in Supporting Information, Table S6. The highest TEQs of ∑PAH16 and ∑PAH7 with mean of 1422.27 μg/kg and 1400.48 μg/kg, respectively, were present in soils adjacent to abandoned oil well with abandoned time of 0—5 years. And the TEQs of ∑PAH16 and ∑PAH7 decreased with the abandoned time though the percentage proportion of PAHs increased. The TEQs of ∑PAH16 and ∑PAH7 were close between abandoned time of 5–10 years and 10—15 years while both had high content. It demonstrated that high ecological risk was persistent in abandoned oil well areas over abandoned time of 15 years, and basically stable after 5 years. Therefore, abandoned oil well areas need to be blocked to prevent PAHs entering the external environment, and combine physical–chemical technology for petroleum remediation instead of simple weathering biological processes.Table 3 Descriptive statistic TEQBap of PAHs in different sampling area.Full size tableAs referred the PAHs standard of Dutch soil, TEQs of ∑PAH7 was 32.02 μg/kg, calculated by ten individual PAHs times TEFs. In this study, the mean TEQs of ∑PAH7 were about 35- and 10-folds of Dutch soil in petro-related area soils and grassland soils, indicating a high and medium ecological risk in these soils respectively. However, the mean TEQs of ∑PAH7 in farmland soils (18.80 μg/kg) was below Dutch soil, presenting a low potential ecological risk. It should be noted that the minimum of TEQs of ∑PAH7 in grassland soil was 26.24 μg/kg less than TEQs of ∑PAH7 in Dutch soil, but it was vulnerable affected by the surrounding soils with high TEQs of ∑PAH7. In this study, except the farmland soils, TEQs of ∑PAH7 exhibited higher TEQ values than those reported soils in Santiago, Chile47 and Nepal24, and road dust in Tianjin, China48. Overall, the most threat of ecological risk in petro-related soils caused by the anthropogenic PAHs input, such like oil leakage, oil refining, and fossil energy combustion. Preventing oil spills accident and developing the remediation methods are the main significant ways to reduce the ecological risks in these areas. The medium ecological risk in grassland might result from the migration of PAHs via rainfall pathway. Therefore, establishment the oil-blocking isolation zones is the critical way for medium ecological risk areas to control petroleum inflow. Even though the low ecological risk was identified in farmland soils, PAHs source analysis indicated that the biomass combustion should be controlled in these areas. More