More stories

  • in

    Mangroves provide blue carbon ecological value at a low freshwater cost

    At least 11 coastal ecosystems have been considered based on a minimum of actionably defined criteria to be “blue carbon ecosystems”. These include mangrove wetlands, tidal marshes (salt, brackish, fresh), seagrasses, salt flats, freshwater (upper estuarine) tidal forests, macroalgae, phytoplankton, coral reef, marine fauna (fish), oyster reefs, and mud flats5; all but three of these would be considered wetlands, with salt flats and mud flats being examples of non-emergent (plant) blue carbon wetlands. Herein, we focus on mangroves.Adjusting intrinsic leaf-level photosynthetic water use efficiency (({WUE}_{int})) in response to environmental gradients (Introduction)We used data provided by B.F. Clough & R.G. Sims20, which presented leaf-scale net photosynthesis (({P}_{n}) [sic]; μmol CO2 m−2 s−1), stomatal conductance (({g}_{w}): mol m−2 s−1), leaf-intercellular CO2 concentrations (({c}_{i}): μl l−1), and intrinsic photosynthetic water use efficiency (({WUE}_{int}): (frac{{P}_{n}}{{g}_{w}})) for 19 mangrove species occupying 9 different sites in Papua New Guinea and northern Australia. These field data were collected using an infrared gas analyzer (model Li-6000, Li-Cor Biosciences, Inc., Lincoln, NE, USA) attached to leaves at saturation light levels (reported as  > 800 μmol PPFD m−2 s−1). Soil salinity at the time of data collection ranged from 10 to 49 psu, and median long-term atmospheric temperature and relative humidity among sites ranged from 19.9 to 27.4 °C and 35.1 to 92.2%, respectively (Fig. S1)20. These data were among the first to offer insight from field study into the plasticity of mangroves across a range of natural salinity and aridity gradients to adjust leaf-level ({WUE}_{int}) as needed for local environmental condition. While it is not new for trees to adjust their ({WUE}_{int}) when they develop in arid, semi-arid, or even some humid and tropical environments60, what is distinctive is that mangroves may be further driven to water savings by salinity gradients as a condition of development.
    ({{varvec{W}}{varvec{U}}{varvec{E}}}_{{varvec{i}}{varvec{n}}{varvec{t}}}) and individual tree water use of mangrove wetlands versus terrestrial ecosystemsFor Fig. 1a, we compare leaf-level ({WUE}_{int}) data collected from 17 published papers (using maximum and minimum values), providing 67 independent measurements of ({WUE}_{int}) for mangroves (Table S3). While we mention in the main text that as many as 214 independent measurements of water use efficiency are available, not all of these present raw ({P}_{n}) or ({g}_{w}) data, with some reporting leaf transpiration (({T}_{r})) which do not enable reporting of intrinsic water use efficiencies. Also, we strategically included studies from reproducible experimental designs and readily available papers. Mangrove species included in this review represented a global distribution of greenhouse and field observations, and encompassed species in the following mangrove genera: Rhizophora, Avicennia, Laguncularia, Bruguiera, Aegialitis, Aegiceras, Ceriops, Sonneratia, Kandelia, Excoecaria, Heritiera, Xylocarpus, and Conocarpus.We then accessed an existing database (n = 11,328 observations) that reported raw ({P}_{n}) and ({g}_{w}) data from 210 upland deciduous and evergreen shrubs and trees of savannah, boreal, temperate, and tropical habitats60. From these data, we evaluated a range of upland tree and shrub species that occurred and developed naturally in environments along a global gradient of vapor pressure deficit (i.e., atmospheric moisture and temperature), including arid, semi-arid, dry semi-humid, and humid locations.For Fig. 1b, we started with a review by Wullschleger et al.61 that provides maximum individual tree water use data (L H2O day−1) from 52 published studies representing 67 species of upland trees from around the world. Of those studies, dbh values (8 to 134 cm) were provided alongside 47 individual tree water use values. Maximum individual tree water use and dbh (4.1 to 45.3 cm) were available from the original source for 8 mangrove studies representing 7 species from French Guiana, Mayotte Island (Indian Ocean), China, Florida (USA), and Louisiana (USA) (Table S4). These represent the extent of published sap flow data that provided both individual tree water use and dbh from mangroves (numerically); e.g., we could not extract specific individual tree water use versus dbh from a Moreton Bay (Australia) study site62, south Florida study site63, or from five additional study sites in China51,52. However, regressions for two of the Chinese study sites provided over two years51 indicated that mangrove trees from a suite of species ranging in dbh from 8 to 24 cm used approximately 0.76 and 9.31 L H2O day−1, or 0.53 L H2O day−1 cm−1 of dbh. These apparent rates were even lower than what was reported as average for mangroves in Fig. 1b of 1.4 L H2O day−1 cm−1. The mangrove species included in this analysis were Avicennia germinans (L.) L., Laguncularia racemosa (L.) C.F. Gaertn., Rhizophora mangle L., Ceriops tagal (Perr.) C.B. Rob., Rhizophora mucronata Lam., Sonneratia apetala Buch.-Ham, and Sonneratia caseolaris (L.) Engl.. Additional comparative mangrove species reported by B. Leng & K.-F. Cao51 included Bruguiera sexangula (Lour.) Poir., Bruguiera sexangula var. rhynchopetala W.C. Ko, Excoecaria agallocha L., Rhizophora apiculata Blume, Sonneratia alba Sm., and Xylocarpus granatum J. Koenig.Estimation of canopy transpiration (({{varvec{E}}}_{{varvec{c}}})) from net primary productivity dataEstimates of carbon uptake from CO2 can provide insight into the water use requirement for that uptake of carbon64. We used leaf-level instantaneous water use efficiency (({WUE}_{ins}): (frac{{P}_{N}}{{T}_{r}})), which relates to net CO2 uptake from leaves of the dominant mangrove forest canopy relative to the specific amount of water used, and developed a predictive relationship (predicted) for determining mangrove net primary productivity (NPP) values from ({E}_{c}) using ({WUE}_{ins}). For A. germinans, L. racemosa, and R. mangle forest components, we used light-saturated, leaf-level ({WUE}_{ins}) values of 3.82 ± 0.3, 4.57 ± 0.3, and 5.15 ± 0.4 mmol CO2 (mol H2O)−1 [± 1 SE], respectively, from mangrove saplings and small trees of south Florida65. ({WUE}_{ins}) values were stratified by species relative to basal area distributions on each south Florida study plot, converted from molar fractions of H20 (from ({E}_{c}) determination) and CO2 to molecular weights, and multiplied by ({WUE}_{ins}) with applicable unit conversions to attain kg CO2 m−2 year−1. This value was multiplied by 0.273 to yield kg C m−2 year−1.This predictive relationship was validated in two independent ways. First, for one of the calibration sites (lower Shark River, Everglades National Park, Florida, USA), we modeled ({E}_{c}) from sap flow data50, determined NPP from ({WUE}_{ins}) calculations relative to the amount of water the stand used, and had independent measurements of net ecosystem exchange (NEE) of CO2 between the mangrove ecosystem and atmosphere from an eddy flux tower66. For this site, NPP estimation and NEE were closely aligned once soil CO2 effluxes were accounted; respiratory CO2 effluxes from soil and pneumatophores were determined to be 1.2 kg C m−2 year−1 from previous study67. Using our NPP estimations from ({WUE}_{ins}) calculations and subtracting soil and pneumatophore CO2 effluxes of 1.2 kg C m−2 for 2004 and 0.8 kg C m−2 for 2005 (partial year), NPP becomes 0.96 kg C m−2 for 2004 and 0.85 kg C m−2 for January to August of 2005 (see Observed 1, Florida on Fig. S2). Our approach underestimated NPP from ({E}_{c}) relative to measurements from eddy covariance by 0.21 kg C m-2 for 2004 (within 17.5% of predicted) and was nearly identical for 2005 (within 0.02 kg C m−2, or 2% of predicted).Second, we wanted to determine whether ({E}_{c})-to-NPP predictions developed on a few sites in south Florida, USA, represented other global sites, so we included an analysis from several mangrove sites in Guangdong Province, China, to represent an entirely different location. Similar to south Florida analyses, we combined data for NPP from co-located sites of ({E}_{c}) determination using sap flow techniques. NPP of the mangrove forests were measured using multiple procedures (including eddy flux) for improved accuracy68,69. The relationships of predicted NPP versus ({E}_{c}) and observed NPP versus ({E}_{c}) did not differ between Florida and China (t = 0.48, p = 0.643).Projecting mangrove ({{varvec{E}}}_{{varvec{c}}}) to other locationsWe reviewed data from 26 published records that report mangrove NPP, or enough data to estimate NPP, from 71 study sites located in the Florida-Caribbean Region (25 sites) and Asia–Pacific Region (46 sites) (Table S5). Table S1 reveals mangrove literature sources used, as well as how NPP was estimated from values provided in the original source; itemizes assumptions for determinations of aboveground NPP, wood production, litter production, and root production from various ratios70; and reveals unit conversions.We then convert NPP to ({E}_{c}) for all 71 sites using the predicted curve in Fig. S2 (Eq. 1, main text), and provide summary results by location in Table S1. Regional (ET) data were extracted from the MODIS Global Evapotranspiration Project (MOD16-A3), which are provided at a resolution of 1-km. The locations of mangrove NPP study sites were identified, assigned to a single 1-km2 grid in MOD16, and (ET) was extracted from that grid and used for ({E}_{c})-to-(ET) comparison. Average (ET) from single cells (1 km2) was combined with the average of up to 8 additional neighboring cells to provide comparative (ET) projections over up to 9 km2 for each location from 2000 to 2013 to compare sensitivity among suites of the specific MODIS16-A3 cells selected over land. When neighboring cells were completely over water, they were excluded since component mangrove forest ({E}_{c}) estimation was not possible from the cells. Estimates of (ET) by individual cells used to compare with mangrove ({E}_{c}) versus up to 9 cells differed by an average of only 43 mm H2O year−1 (± 16 mm H2O year−1, S.E.). Therefore, we use (ET) from individual, overlapping ({E}_{c}) cells in Table S1.The average ({E}_{c})-to-(ET) ratio from mangroves was subtracted from ({E}_{c})-to-(ET) ratio for specific ecoregions48, and this ratio difference was assumed to represent net water use strategy affecting differences by the dominant vegetation between ecosystem types. We were also mindful that salinity reductions can affect ({E}_{c}). We used scaled (0–1) mean and standard deviations from ({WUE}_{int}) data previously reported for mangroves (Fig. S1)20. Standard deviation was 32% of mean ({WUE}_{int}) related to salinity gradients, and if we re-scale this deviation to ({E}_{c}) data and add it to the mean ({E}_{c}) to assume low salinity, average ({E}_{c})-to-(ET) ratio becomes 57.4%. This is theoretical and assumes a relatively linear relationship between ({WUE}_{int}) and ({E}_{c}).Comparative water use scaling among ecoregionsTable 1 presents the projected reduction in water used through ({E}_{c}) if a mangrove ({E}_{c})-to-(ET) ratio was applied to tropical rainforest (290.52 mm H2O year−1), temperate deciduous forest (131.76 mm H2O year−1), tropical grassland (110.77 mm H2O year−1), temperate grassland (46.48 mm H2O year−1), temperate coniferous forest (54.96 mm H2O year−1), desert (22.99 mm H2O year−1), and Mediterranean shrubland (12.08 mm H2O year−1). To convert potential water use differences to kL H2O ha−1 year−1 (as presented in the abstract), the following calculation is used (using the example of tropical rainforest):$$frac{290.52 L {H}_{2}O {year}^{-1}}{1 {m}^{2}} times frac{mathrm{10,000 }{m}^{2}}{1 ha} times frac{1 kL {H}_{2}O}{mathrm{1000 }L {H}_{2}O} =mathrm{2905 } kL {H}_{2}O {ha}^{-1}{year}^{-1}$$
    (2)
    For comparisons made to mature ( > 12 years) oil palm (Elaeis guineensis Jacq.) plantations, ({E}_{c})-to-(ET) ratio was assumed to range from 5332 to 70%33, for a water use difference of 1170 and 3160 kL H2O ha−1 year−1, respectively, relative to annual global mangrove (ET) (of 1172 mm). We multiply these values by the 18,467 ha of land area that was converted from mangroves to oil palm31 to attain potential water use differences of 21.6 to 58.4 GL H2O year−1 from avoided conversion of mangrove to oil palm in this region.Global water use scalingIn order to determine how much global mangrove area is adjacent to each ecoregion, we conducted a cross-walk between terrestrial ecoregions71 and those used by Global Mangrove Watch in the 2010 classification of global mangrove area72. Terrestrial ecoregions used by Schlesinger & Jasechko48 were then able to be associated with specific mangrove areas (Table S6). In other words, given a specific ecoregion, we determined how much mangrove area would be occurring within that same ecoregional geography. Global mangrove area assignment to those ecoregions mapped within 0.1% of the total mangrove area of 13,760,000 ha reported in Bunting et al.72. To convert kL H2O ha−1 year−1 to GL H2O year−1 among ecoregions, the following calculation was used (continuing with the example of tropical rainforest, which has an area of adjacent mangroves of 112,331.9 km2):$$frac{mathrm{2905} kL {H}_{2}O {year}^{-1}}{1 ha} times frac{100 ha}{1 {km}^{2}} times frac{mathrm{112,331.90} {km}^{2 }mangroves}{1.0 times {10}^{6} kL {H}_{2}0} times frac{1 GL {H}_{2}O}{1} = mathrm{32,632.42} GL {H}_{2}O {year}^{-1}$$
    (3)
    Agent-based modelling of individual tree water use (Discussion)The BETTINA model simulates the growth of mangrove trees as a response to above- and below-ground resources, i.e. light and water41. In the model, an individual tree is described by four geometric measures, including stem radius, stem height, crown radius and root radius; attributing functional relevance in terms of resource uptake. Aiming to maximize resource uptake, new biomass is allocated to increase these measures in an optimal but not constant proportion. Water uptake of the tree is driven by the water potential gradient between the soil and the leaves. Thus, porewater salinity is part of what determines the water availability for plants.With the BETTINA model, we simulated the growth of nine individual mangrove trees under different salinity conditions, ranging between 0 and 80 psu, while all other environmental and tree-specific conditions were kept constant. Simulation time was 200 years so that trees could achieve very close to their maximum possible size, and the hydrological parameters were similar to that reported previously42. We can show that the ratio of the actual transpiration to the potential transpiration decreases with increasing salinity; plants use less water. Potential transpiration was the transpiration of a given tree without a simulated reduction in water availability due to porewater salinity. These parameter details are presented graphically for mangroves (Fig. S3), comparing porewater salinity along a gradient against the ratio of actual-to-potential individual tree transpiration.Further, BETTINA simulation results include morphological plasticity adjustments to allometry. To highlight this, we also displayed results assuming a constant allometry as for 40 psu. Naturally, for this arbitrary benchmark the solid and the dashed line coincide (Fig. 3a). Adaptation to higher salinities improves water uptake (primarily girth and root growth), thus the adapted trees (solid lines) have a higher water uptake than the average allometry (dashed lines) for salinities below 40 psu. Lower salinities promote increase of height and crown radius to improve light availability. That is why the adapted trees have a lower water uptake than an average tree would for salinities above 40 psu. Tree water use decreases with increasing salinity (Fig. 3b), as ({WUE}_{int}) coincidently increases (Fig. S1).Virtual water use explained (Discussion)Water is required to produce products or acquire services from natural ecosystems; e.g., forest products, fisheries biomass, nutrient processing (nitrification, denitrification), food production. If a net kilogram of a food is grown on a hectare of land where water is abundant and that kilogram of food requires 400 mm of water to be produced, the export of that food to an area of low water availability provides an ecosystem service in the amount of 1 kg of food, plus 400 mm of “virtual” water not actually needed at the destination but used at the source. This water is defined as the product’s “virtual water content”56. There is a rich body of literature exploring the concept of virtual water73,74, but we expand on this concept here as a comparison among 7 ecoregions48 and mangroves. Raw data used for calculations are presented in Table S2.Statistical analysisData for leaf-level ({WUE}_{int}) comparisons between terrestrial woody plants and mangroves, as well as individual tree water use by dbh for both terrestrial and mangrove trees, were not normally distributed. We used a Kruskal–Wallis ANOVA based on ranks, and the Dunn’s Method for difference tests. Individual tree water use by dbh for both terrestrial and mangrove trees were determined using linear regression, mostly applied to mean values. For a couple of mangrove studies, only median values were extractable from minimum and maximum values. Likewise, all other data relationships were best fit with linear models, including the calibration curves between ({E}_{c}) and NPP. All data were analyzed using SigmaPlot (v. 14.0, Systat, Inc., Palo Alto, California, USA). More

  • in

    Urban ecosystem drives genetic diversity in feral honey bee

    United Nations, Department of Economic and Social Affairs & Population Division. World Urbanization Prospects: The 2018 Revision (ST/ESA/SER.A/420). (United Nations, 2019).Wei, Y. D. & Ewing, R. Urban expansion, sprawl and inequality. Landsc. Urban Plan. 177, 259–265. https://doi.org/10.1016/j.landurbplan.2018.05.021 (2018).Article 

    Google Scholar 
    Ayers, A. C. & Rehan, S. M. Supporting bees in cities: How bees are influenced by local and landscape features. Insects 12 (2021).Grimm, N. B. et al. Global change and the ecology of cities. Science 319, 756–760. https://doi.org/10.1126/science.1150195 (2008).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515. https://doi.org/10.1146/annurev.ecolsys.34.011802.132419 (2003).Article 

    Google Scholar 
    Shochat, E. et al. Invasion, competition, and biodiversity loss in urban ecosystems. Bioscience 60, 199–208. https://doi.org/10.1525/bio.2010.60.3.6 (2010).Article 

    Google Scholar 
    Sánchez-Bayo, F. & Wyckhuys, K. A. G. Worldwide decline of the entomofauna: A review of its drivers. Biol. Cons. 232, 8–27. https://doi.org/10.1016/j.biocon.2019.01.020 (2019).Article 

    Google Scholar 
    Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674. https://doi.org/10.1038/s41586-019-1684-3 (2019).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Wagner, D. L. Insect declines in the anthropocene. Annu. Rev. Entomol. 65, 457–480. https://doi.org/10.1146/annurev-ento-011019-025151 (2020).Article 
    CAS 
    PubMed 

    Google Scholar 
    Brown, M. J. & Paxton, R. J. The conservation of bees: A global perspective. Apidologie 40, 410–416 (2009).
    Google Scholar 
    Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Kennedy, C. M. et al. A global quantitative synthesis of local and landscape effects on wild bee pollinators in agroecosystems. Ecol. Lett. 16, 584–599 (2013).PubMed 

    Google Scholar 
    Potts, S. G. et al. Summary for policymakers of the assessment report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services on pollinators, pollination and food production. (2016).Winfree, R., Aguilar, R., Vázquez, D. P., LeBuhn, G. & Aizen, M. A. A meta-analysis of bees’ responses to anthropogenic disturbance. Ecology 90, 2068–2076 (2009).PubMed 

    Google Scholar 
    Millard, J. et al. Global effects of land-use intensity on local pollinator biodiversity. Nat. Commun. 12, 1–11 (2021).ADS 

    Google Scholar 
    Baldock, K. C. et al. A systems approach reveals urban pollinator hotspots and conservation opportunities. Nat. Ecol. Evolut. 3, 363–373 (2019).
    Google Scholar 
    Banaszak-Cibicka, W., Twerd, L., Fliszkiewicz, M., Giejdasz, K. & Langowska, A. City parks vs. natural areas—Is it possible to preserve a natural level of bee richness and abundance in a city park?. Urban Ecosyst. 21, 599–613 (2018).
    Google Scholar 
    Hall, D. M. et al. The city as a refuge for insect pollinators. Conserv. Biol. 31, 24–29 (2017).PubMed 

    Google Scholar 
    Theodorou, P. et al. Urban areas as hotspots for bees and pollination but not a panacea for all insects. Nat. Commun. 11, 1–13 (2020).
    Google Scholar 
    Wilson, C. J. & Jamieson, M. A. The effects of urbanization on bee communities depends on floral resource availability and bee functional traits. PLoS ONE 14, e0225852 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Samuelson, A. E., Schürch, R. & Leadbeater, E. Dancing bees evaluate central urban forage resources as superior to agricultural land. J. Appl. Ecol. 59, 79–88 (2022).
    Google Scholar 
    Fortel, L. et al. Decreasing abundance, increasing diversity and changing structure of the wild bee community (Hymenoptera: Anthophila) along an urbanization gradient. PLoS ONE 9, e104679 (2014).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Roffet-Salque, M. et al. Widespread exploitation of the honeybee by early Neolithic farmers. Nature 527, 226–230 (2015).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Crane, E. Recent research on the world history of beekeeping. Bee World 80, 174–186 (1999).
    Google Scholar 
    Dietemann, V., Pirk, C. W. W. & Crewe, R. Is there a need for conservation of honeybees in Africa?. Apidologie 40, 285–295 (2009).
    Google Scholar 
    Jaffe, R. et al. Estimating the density of honeybee colonies across their natural range to fill the gap in pollinator decline censuses. Conserv. Biol. 24, 583–593 (2010).PubMed 

    Google Scholar 
    Browne, K. A. et al. Investigation of free-living honey bee colonies in Ireland. J. Apic. Res. 60, 229–240. https://doi.org/10.1080/00218839.2020.1837530 (2021).Article 

    Google Scholar 
    Kohl, P. L. & Rutschmann, B. The neglected bee trees: European beech forests as a home for feral honey bee colonies. PeerJ 6, e4602 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Oleksa, A., Gawroński, R. & Tofilski, A. Rural avenues as a refuge for feral honey bee population. J. Insect Conserv. 17, 465–472 (2013).
    Google Scholar 
    Rutschmann, B., Kohl, P. L., Machado, A. & Steffan-Dewenter, I. Semi-natural habitats promote winter survival of wild-living honeybees in an agricultural landscape. Biol. Cons. 266, 109450 (2022).
    Google Scholar 
    Thompson, C. E., Biesmeijer, J. C., Allnutt, T. R., Pietravalle, S. & Budge, G. E. Parasite pressures on feral honey bees (Apis mellifera sp). PLoS ONE 9, e105164 (2014).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bila Dubaić, J. et al. Unprecedented density and persistence of feral honey bees in urban environments of a large SE-European City (Belgrade, Serbia). Insects 12, 1127 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Alaux, C., Le Conte, Y. & Decourtye, A. Pitting wild bees against managed honey bees in their native range, a losing strategy for the conservation of honey bee biodiversity. Front. Ecol. Evol. 7, 60 (2019).
    Google Scholar 
    Requier, F. et al. The conservation of native honey bees is crucial. Trends Ecol. Evol. 34, 789–798 (2019).PubMed 

    Google Scholar 
    Mladenović, S. et al. Environment in Belgrade in 2018. (in Serbian: Kvalitet životne sredine u Beogradu u 2018. godini). (The City Administration, Secretariat for Environmental Protection, 2019).Statistical Office of the Republic of Serbia. https://data.stat.gov.rs/Home/Result/130202010207?languageCode=en-US.
    (“The Official Gazette of the Republic of Serbia”, Nos. 41/2009, 93/2012 and 14/2106 [In Serbian], 2009).Johnson, M. T. & Munshi-South, J. Evolution of life in urban environments. Science 358, eaam8327 (2017).PubMed 

    Google Scholar 
    Jara, L. et al. Stable genetic diversity despite parasite and pathogen spread in honey bee colonies. Sci. Nat. 102, 1–8 (2015).
    Google Scholar 
    Tanasković, M. et al. MtDNA analysis indicates human-induced temporal changes of serbian honey bees diversity. Insects 12, 767 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Wang, J. COANCESTRY: A program for simulating, estimating and analysing relatedness and inbreeding coefficients. Mol. Ecol. Resour. 11, 141–145. https://doi.org/10.1111/j.1755-0998.2010.02885.x (2011).Article 
    PubMed 

    Google Scholar 
    Wang, J. Triadic IBD coefficients and applications to estimating pairwise relatedness. Genet. Res. 89, 135–153. https://doi.org/10.1017/s0016672307008798 (2007).Article 
    CAS 
    PubMed 

    Google Scholar 
    Jacobson, S. Locally adapted, varroa resistant honey bees: ideas from several key studies. Am. Bee J. (2010).McNeely, J. A., Miller, K. R., Reid, W. V., Mettermeier, R. A. & Werner, T. B. Conserving the world’s biological diversity. (UICN, Morges (Suiza) WRI, Washington DC (EUA) CI, Washington DC (EUA) WWF …, 1990).Hoban, S. M. et al. Bringing genetic diversity to the forefront of conservation policy and management. Conserv. Genet. Resour. 5, 593–598 (2013).
    Google Scholar 
    Hohenlohe, P. A., Funk, W. C. & Rajora, O. P. Population genomics for wildlife conservation and management. Mol. Ecol. 30, 62–82 (2021).PubMed 

    Google Scholar 
    Shafer, A. B. et al. Genomics and the challenging translation into conservation practice. Trends Ecol. Evol. 30, 78–87 (2015).PubMed 

    Google Scholar 
    Mattila, H. R. & Seeley, T. D. Genetic diversity in honey bee colonies enhances productivity and fitness. Science 317, 362–364 (2007).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Oddie, M. A. & Dahle, B. Insights from Norway: Using natural adaptation to breed Varroa-resistant honey bees. Bee World 98, 38–43 (2021).
    Google Scholar 
    Oddie, M. A., Dahle, B. & Neumann, P. Norwegian honey bees surviving Varroa destructor mite infestations by means of natural selection. PeerJ 5, e3956 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Oldroyd, B. P. & Fewell, J. H. Genetic diversity promotes homeostasis in insect colonies. Trends Ecol. Evol. 22, 408–413 (2007).PubMed 

    Google Scholar 
    Tarpy, D. R. Genetic diversity within honeybee colonies prevents severe infections and promotes colony growth. Proc. R Soc. Lond. Series B Biol. Sci. 270, 99–103 (2003).
    Google Scholar 
    van Baalen, M. & Beekman, M. The costs and benefits of genetic heterogeneity in resistance against parasites in social insects. Am. Nat. 167, 568–577 (2006).PubMed 

    Google Scholar 
    Eckholm, B. J., Anderson, K. E., Weiss, M. & DeGrandi-Hoffman, G. Intracolonial genetic diversity in honeybee (Apis mellifera) colonies increases pollen foraging efficiency. Behav. Ecol. Sociobiol. 65, 1037–1044 (2011).
    Google Scholar 
    Graham, S., Myerscough, M., Jones, J. & Oldroyd, B. Modelling the role of intracolonial genetic diversity on regulation of brood temperature in honey bee (Apis mellifera L.) colonies. Insectes Soc. 53, 226–232 (2006).
    Google Scholar 
    Jones, J. C., Myerscough, M. R., Graham, S. & Oldroyd, B. P. Honey bee nest thermoregulation: Diversity promotes stability. Science 305, 402–404 (2004).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Tanasković, M. et al. Further evidence of population admixture in the Serbian honey bee population. Insects 13, 180 (2022).PubMed 
    PubMed Central 

    Google Scholar 
    Nedić, N. et al. Detecting population admixture in honey bees of Serbia. J. Apic. Res. 53, 303–313. https://doi.org/10.3896/IBRA.1.53.2.12 (2014).Article 

    Google Scholar 
    Nedić, N., Stanisavljević, L., Mladenović, M. & Stanisavljević, J. Molecular characterization of the honeybee Apis mellifera carnica in Serbia. Arch. Biol. Sci. 61, 587–598 (2009).
    Google Scholar 
    Kükrer, M., Kence, M. & Kence, A. Honey bee diversity is swayed by migratory beekeeping and trade despite conservation practices: Genetic evidence for the impact of anthropogenic factors on population structure. Front. Ecol. Evolut. 9 (2021).Bouga, M., Harizanis, P. C., Kilias, G. & Alahiotis, S. Genetic divergence and phylogenetic relationships of honey bee Apis mellifera (Hymenoptera: Apidae) populations from Greece and Cyprus using PCR–RFLP analysis of three mtDNA segments. Apidologie 36, 335–344 (2005).CAS 

    Google Scholar 
    Dall’Olio, R., Marino, A., Lodesani, M. & Moritz, R. F. Genetic characterization of Italian honeybees, Apis mellifera ligustica, based on microsatellite DNA polymorphisms. Apidologie 38, 207–217 (2007).CAS 

    Google Scholar 
    Neumann, P. & Blacquière, T. The Darwin cure for apiculture? Natural selection and managed honeybee health. Evol. Appl. 10, 226–230 (2017).PubMed 

    Google Scholar 
    Kulinčević, J., Rinderer, T., Mladjan, V. & Buco, S. Five years of bi-directional genetic selection for honey bees resistant and susceptible to Varroa jacobsoni. Apidologie 23, 443–452 (1992).
    Google Scholar 
    2011 Census of Population, Households and Dwellings in the Republic of Serbia: Comparative Overview of the Number of Population in 1948, 1953, 1961, 1971, 1981, 1991, 2002 and 2011. (Statistical Office of the Republic of Serbia, 2014).Techer, M. A. et al. Large-scale mitochondrial DNA analysis of native honey bee Apis mellifera populations reveals a new African subgroup private to the South West Indian Ocean islands. BMC Genet. 18, 1–21 (2017).
    Google Scholar 
    Garnery, L., Cornuet, J. M. & Solignac, M. Evolutionary history of the honey bee Apis mellifera inferred from mitochondrial DNA analysis. Mol. Ecol. 1, 145–154 (1992).CAS 
    PubMed 

    Google Scholar 
    Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Excoffier, L. & Lischer, H. E. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resources. 10, 564–567 (2010).
    Google Scholar 
    Kalinowski, S. T. hp-rare 1.0: A computer program for performing rarefaction on measures of allelic richness. Mol. Ecol. Notes. 5, 187–189 (2005).CAS 

    Google Scholar 
    Stoneking, M., Hedgecock, D., Higuchi, R. G., Vigilant, L. & Erlich, H. A. Population variation of human mtDNA control region sequences detected by enzymatic amplification and sequence-specific oligonucleotide probes. Am. J. Hum. Genet. 48, 370 (1991).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hammer, Ø., Harper, D. A. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
    Google Scholar 
    Crozier, R. & Crozier, Y. The mitochondrial genome of the honeybee Apis mellifera: Complete sequence and genome organization. Genetics 133, 97–117 (1993).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: Linked loci and correlated allele frequencies. Genetics 164, 1567–1587. https://doi.org/10.1093/genetics/164.4.1567 (2003).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: Dominant markers and null alleles. Mol. Ecol. Notes 7, 574–578 (2007).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hubisz, M. J., Falush, D., Stephens, M. & Pritchard, J. K. Inferring weak population structure with the assistance of sample group information. Mol. Ecol. Resour. 9, 1322–1332. https://doi.org/10.1111/j.1755-0998.2009.02591.x (2009).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Earl, D. A. & VonHoldt, B. M. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).
    Google Scholar 
    Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).CAS 
    PubMed 

    Google Scholar 
    Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: A new method for the analysis of genetically structured populations. BMC Genet. 11, 94. https://doi.org/10.1186/1471-2156-11-94 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ward, J. H. Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58, 236–244. https://doi.org/10.1080/01621459.1963.10500845 (1963).Article 
    MathSciNet 

    Google Scholar 
    Wang, J. An estimator for pairwise relatedness using molecular markers. Genetics 160, 1203–1215. https://doi.org/10.1093/genetics/160.3.1203 (2002).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Li, C. C., Weeks, D. E. & Chakravarti, A. Similarity of DNA fingerprints due to chance and relatedness. Hum. Hered. 43, 45–52. https://doi.org/10.1159/000154113 (1993).Article 
    CAS 
    PubMed 

    Google Scholar 
    Lynch, M. Estimation of relatedness by DNA fingerprinting. Mol. Biol. Evol. 5, 584–599. https://doi.org/10.1093/oxfordjournals.molbev.a040518 (1988).Article 
    CAS 
    PubMed 

    Google Scholar 
    Lynch, M. & Ritland, K. Estimation of pairwise relatedness with molecular markers. Genetics 152, 1753–1766. https://doi.org/10.1093/genetics/152.4.1753 (1999).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ritland, K. Estimators for pairwise relatedness and individual inbreeding coefficients. Genet. Res. 67, 175–185 (1996).
    Google Scholar 
    Queller, D. C. & Goodnight, K. F. Estimating relatedness using genetic markers. Evolution 43, 258–275. https://doi.org/10.1111/j.1558-5646.1989.tb04226.x (1989).Article 
    PubMed 

    Google Scholar 
    Milligan, B. G. Maximum-likelihood estimation of relatedness. Genetics 163, 1153–1167 (2003).PubMed 
    PubMed Central 

    Google Scholar 
    del Felipe, P. et al. Genetic diversity and structure of the commercially important native fish pacu (Piaractus mesopotamicus) from cultured and wild fish populations: Relevance for broodstock management. Aquacult. Int. 29, 289–305. https://doi.org/10.1007/s10499-020-00626-w (2021).Article 

    Google Scholar  More

  • in

    Characterization of Pseudoterranova ceticola (Nematoda: Anisakidae) larvae from meso/bathypelagic fishes off Macaronesia (NW Africa waters)

    Buchmann, K. & Mehrdana, F. Effects of anisakid nematodes Anisakis simplex (s.l.), Pseudoterranova decipiens (s.l.) and Contracaecum osculatum (s.l.) on fish and consumer health. Food Waterborne Parasitol. 4, 13–22. https://doi.org/10.1016/j.fawpar.2016.07.003 (2016).Article 

    Google Scholar 
    Mattiucci, S., Cipriani, P., Levsen, A., Paoletti, M. & Nascetti, G. Molecular epidemiology of Anisakis and anisakiasis: An ecological and evolutionary road map. Adv. Parasitol. https://doi.org/10.1016/bs.apar.2017.12.001 (2018).Article 
    PubMed 

    Google Scholar 
    Mattiucci, S., Cipriani, P., Paoletti, M., Levsen, A. & Nascetti, G. Reviewing biodiversity and epidemiological aspects of anisakid nematodes from the North-east Atlantic Ocean. J. Helminthol. https://doi.org/10.1017/S0022149X1700027X (2017).Article 
    PubMed 

    Google Scholar 
    Moravec, F. & Justine, J.-L. Erection of Euterranova n. gen. and Neoterranova n. gen. (Nematoda, Anisakidae), with the description of E. dentiduplicata n. sp. and new records of two other anisakid nematodes from sharks off New Caledonia. Parasite 27, 58. https://doi.org/10.1051/parasite/2020053 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Shamsi, S. & Suthar, J. Occurrence of Terranova larval types (nematoda: Anisakidae) in Australian marine fish with comments on their specific identities. Peer J. 4, e1722. https://doi.org/10.7717/peerj.1722 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Timi, J. T. et al. Molecular identification, morphological characterization and new insights into the ecology of larval Pseudoterranova cattani in fishes from the Argentine coast with its differentiation from the Antarctic species, P. decipiens sp. E (Nematoda: Anisakidae). Vet. Parasitol. 199, 59–72 (2014).CAS 
    PubMed 

    Google Scholar 
    Deardorff, T. L. Redescription of Pulchrascaris chiloscyllii (Johnston and Mawson, 1951) (Nematoda: Anisakidae), with comments on species in Pulchrascaris and Terranova. Proc. Helminthol. Soc. Wash. 54, 28–39 (1987).
    Google Scholar 
    Cannon, L. R. G. Some larval ascaridoids from south-eastern queensland marine fishes. Int. J. Parasitol. 7, 233–243 (1977).CAS 
    PubMed 

    Google Scholar 
    Levsen, A. & Lunestad, B. T. Anisakis simplex third stage larvae in Norwegian spring spawning herring (Clupea harengus L.), with emphasis on larval distribution in the flesh. Vet. Parasitol. 171, 247–253 (2010).PubMed 

    Google Scholar 
    Berland, B., (1989) Identification of fish larval nematodes from fish. In: Möller H, editor. Nematode problems in North Atlantic fish. Report from a workshop in Kiel, 3 4 16–22.Zhu, X., D’Amelio, S., Paggi, L. & Gasser, R. B. Assessing sequence variation in the internal transcribed spacers of ribosomal DNA within and among members of the Contracaecum osculatum complex (nematoda: Ascaridoidea: Anisakidae). Parasitol. Res. 86, 677–683 (2000).CAS 
    PubMed 

    Google Scholar 
    Nadler, S. A. & Hudspeth, D. S. S. Phylogeny of the ascaridoidea (Nematoda: Ascaridida) based on three genes and morphology hypotheses of structural and sequence evolution. J. Parasitol. 86, 380–393. https://doi.org/10.1645/0022-3395(2000)086[0380:POTANA]2.0.CO;2 (2000).Article 
    CAS 
    PubMed 

    Google Scholar 
    Mattiucci, S. et al. Genetic and morphological approaches distinguish the three sibling species of the Anisakis simplex species complex, with a species designation as Anisakis berlandi n. sp. for A simplex sp. C (Nematoda: Anisakidae). J. Parasitol. 100, 199–214. https://doi.org/10.1645/12-120.1 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
    PubMed 

    Google Scholar 
    Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nagy, L. G. et al. Re-mind the gap! Insertion – deletion data reveal neglected phylogenetic potential of the nuclear ribosomal internal transcribed spacer (ITS) of fungi. PLoS ONE 7, 1–9 (2012).
    Google Scholar 
    Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 4, 1–5 (2018).
    Google Scholar 
    Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in bayesian phylogenetics using tracer 1.7. Syst. Biol. 67, 901–904 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cipriani, P. et al. Anisakid nematodes in Trichiurus lepturus and Saurida undosquamis (Teleostea) from the South-West Indian Ocean : Genetic evidence for the existence of sister species within Anisakis typica (s.l.), and food-safety considerations. Food Waterborne Parasitol. 28, e00177. https://doi.org/10.1016/j.fawpar.2022.e00177 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Safonova, A. E. First report on molecular identification of Anisakis simplex in Oncorhynchus nerka from the fish market, with taxonomical issues within Anisakidae. J. Nematol. 53(1), 10. https://doi.org/10.21307/jofnem-2021-023 (2021).Article 
    CAS 

    Google Scholar 
    Takano, T. & Sata, N. Multigene phylogenetic analysis reveals non-monophyly of Anisakis s.l. and Pseudoterranova (Nematoda: Anisakidae). Parasitol. Int. 91, 102631. https://doi.org/10.1016/j.parint.2022.102631 (2022).Article 
    CAS 
    PubMed 

    Google Scholar 
    Leiper, R. T. & Atkinson, E. L. Parasitic worms, with a note on a free-living nematode. British Museum (Natural History). Bristish Antarctic (“Terra Nova”) expedition, 1910. Natural History Report. Zool 2(3), 19–60 (1915).
    Google Scholar 
    Leiper, R. T. & Atkinson, E. L. Helminthes of the British Antarctic expedition 1910–1913. Proc. Zool. Soc. London, 222–226 (1914).Myers, B. J. Phocanema, a new genus for the anisakid nematode of seals. Can. J. Zool. 37, 459–465 (1959).
    Google Scholar 
    Mattiucci, S., Paoletti, M., Webb, S. C. & Nascetti, G. Pseudoterranova and Contracaecum. In Molecular detection of human parasitic pathogens (ed. Liu, D.) 645–656 (CRC Press, 2012).
    Google Scholar 
    Mozgovoĭ, A.A., (1953) Ascaridata of animals and man, and the diseases caused by them. In: Osnovy nematodologii. Vol. II. Izd. AN SSSR, Moskva (In Russian)Johnston, T.H., Mawson, P.M., (1939) Internal parasites of the pigmy sperm whale. Rec. South Aust Museum.6. http://www.biodiversitylibrary.org/item/126147.Gibson, D. I. The systematics of ascaridoid nematodes-a current assessment. In Stone A (eds Platt, H. & Khalil, L.) 321–338 (Academic Press, 1983).
    Google Scholar 
    Shamsi, S., Barton, D. P. & Zhu, X. Description and characterisation of Terranova pectinolabiata n. sp. (Nematoda: Anisakidae) in great hammerhead shark, Sphyrna mokarran (Rüppell, 1837), in Australia. Parasitol. Res. 118, 2159–2168. https://doi.org/10.1007/s00436-019-06360-4 (2019).Article 
    PubMed 

    Google Scholar 
    Shamsi, S., Barton, D. P. & Zhu, X. Description and genetic characterisation of Pulchrascaris australis n. sp. in the scalloped hammerhead shark, Sphyrna lewini (Griffin & Smith) in Australian waters. Parasitol. Res. https://doi.org/10.1007/s00436-020-06672-w (2020).Article 
    PubMed 

    Google Scholar 
    González-Solís, D. et al. Parasitic nematodes of marine fishes from Palmyra Atoll, East Indo-Pacific, including a new species of Spinitectus (Nematoda, Cystidicolidae). Zookeys. 2019, 1–26 (2019).
    Google Scholar 
    Jabbar, A. et al. Larval anisakid nematodes in teleost fishes from Lizard Island, northern great barrier reef Australia. Mar. Freshw. Res. 63, 1283. https://doi.org/10.1071/MF12211 (2012).Article 

    Google Scholar 
    ICES. (2012) Pseudoterranova larvae (“codworm”; Nematoda) in fish. Revised and updated by Matt Longshaw. ICES Identification Leaflets for diseases and parasites of fish and shellfish. Leaflet No. 7. 4 pp.Arai, H. P. & Smith, J. W. Guide to the parasites of fishes of Canada part V: Nematoda. Zootaxa 4185, 1. https://doi.org/10.11646/zootaxa.4185.1.1 (2016).Article 

    Google Scholar 
    Hurst, H. J. Identification and description of larval Anisakis simplex and Pseudoterranova decipiens (anisakidae: Nematoda) from New Zealand waters. New Zeal J. Mar. Freshw. Res. 18, 177–186 (1984).
    Google Scholar 
    Hernández-Orts, J. S. et al. Description, microhabitat selection and infection patterns of sealworm larvae (Pseudoterranova decipiens species complex, Nematoda: Ascaridoidea) in fishes from Patagonia Argentina. Parasite Vector. 6, 1–15 (2013).
    Google Scholar 
    Shiraki, T. Larval nematodes of family anisakidae (Nematoda) in the northern sea of Japan as a causative agent of eosinophilic phlegmone of granuloma in the human gastro-intestinal tract. Acta Med. Biol. 22, 57–98 (1974).
    Google Scholar 
    Berland, B. Nematodes from some Norwegian marine fishes. Sarsia 2, 1–50. https://doi.org/10.1080/00364827.1961.10410245 (1961).Article 

    Google Scholar 
    George-Nascimento, M. & Llanos, A. Micro-evolutionary implications of allozymic and morphometric variations in sealworms Pseudoterranova sp. (Ascaridoidea: Anisakidae) among sympatric hosts from the Southeastern Pacific Ocean. Int. J. Parasitol. 25, 1163–1171 (1995).CAS 
    PubMed 

    Google Scholar 
    Deardorff, T. L., Kliks, M. M., Rosenfeld, M. E., Rychlinski, R. A. & Desowitz, R. S. Larval, ascaridoid nematodes from fishes near the Hawaiian Islands, with commonents on pathogenicity experiments. Pacific Sci. 36, 187–201 (1982).
    Google Scholar 
    Deardorff, T. L., Kliks, M. M. & Desowitz, R. S. Histopathology induced by larval Terranova (Type HA) (nematoda: Anisakinae) in experimentally infected rats. J. Parasitol. 69, 191–195 (1983).CAS 
    PubMed 

    Google Scholar 
    Kuramochi, T. et al. Stomach nematodes of the family anisakidae collected from the cetaceans stranded on or incidentally caught off the coasts of the Kanto districts and adjoining areas. Mem. Nat. Museum. Nat. Sci. 37, 177–192 (2001).
    Google Scholar 
    Deardorff, T. L., Raybourne, R. B. & Desowitz, R. S. Description of a third-stage larva, Terranova type Hawaii A (nematoda: Anisakinae), from Hawaiian fishes. J. Parasitol. 70, 829–831 (1984).CAS 
    PubMed 

    Google Scholar 
    González-Solís, D., Vidal-Martínez, V. M., Antochiw-Alonso, D. M. & Ortega-Argueta, A. Anisakid nematodes from stranded pygmy sperm whales, Kogia breviceps (Kogiidae), in three localities of the Yucatan peninsula. Mexico. J. Parasitol. 92, 1120–1122 (2006).
    Google Scholar 
    Santos, C. P. & Lodi, L. Occurrence of Anisakis physeteris Baylis, 1923 and Pseudoterranova sp. (Nematoda) in pygmy sperm whale Kogia breviceps (De Blainvillei, 1838) (Physeteridae) in northeastern coast of Brazil. Mem. Inst. Oswaldo Cruz. 93, 187–188. https://doi.org/10.1590/s0074-02761998000200009 (1998).Article 
    CAS 
    PubMed 

    Google Scholar 
    Bloodworth, B. E. & Odell, D. K. Kogia breviceps (cetacea: Kogiidae). Mam. Species. 819, 1–12. https://doi.org/10.1644/819.1 (2008).Article 

    Google Scholar 
    Deardorff, T. L. & Overstreet, R. M. Terranova ceticola n. sp. (Nematoda: Anisakidae) from the dwarf sperm whale; Kogia simus (Owen), in the Gulf of Mexico. Syst. Parasitol. 3, 25–28 (1981).
    Google Scholar 
    Abollo, E., Santiago, P., (2002) SEM study of Anisakis brevispiculata Dollfus, 1966 and Pseudoterranova ceticola (Deardoff and Overstreet, 1981) (Nematoda: Anisakidae), parasites of the pygmy sperm whale Kogia breviceps. Sci. Mar. 66 3 49 255Di Deco, M. A., Orecchia, P., Paggi, L. & Petrarca, V. Morphometric stepwise discriminant analysis of three genetically identified species within Pseudoterranova decipiens (Krabbe, 1878) (Nematoda: Ascaridida). Syst. Parasitol. 29, 81–88 (1994).
    Google Scholar 
    George-Nascimento, M. & Urrutia, X. Pseudoterranova cattani sp. nov. (Ascaridoidea: Anisakidae), a parasite of the South American sea lion Otaria byronia De Blainville from Chile. Rev. Chil. Hist. Nat. 73, 93–98. https://doi.org/10.4067/s0716-078×2000000100010 (2000).Article 

    Google Scholar 
    Mattiucci, S. et al. Allozyme and morphological identification of Anisakis, Contracaecum and Pseudoterranova from Japanese waters (Nematoda, Ascaridoidea). Syst Parasitol. 40, 81–92 (1998).
    Google Scholar 
    Paggi, L. et al. Pseudoterranova decipiens species A and B (Nematoda, Ascaridoidea): Nomenclatural designation, morphological diagnostic characters and genetic markers. Syst. Parasitol. 45, 185–197. https://doi.org/10.1023/A:1006296316222 (2000).Article 
    CAS 
    PubMed 

    Google Scholar 
    Valentini, A. et al. Genetic relationships among Anisakis species (Nematoda: Anisakidae) inferred from mitochondrial cox2 sequences, and comparison with allozyme data. J. Parasitol. 92, 156–166 (2006).CAS 
    PubMed 

    Google Scholar 
    Colón-Llavina, M. M. et al. Additional records of metazoan parasites from Caribbean marine mammals, including genetically identified anisakid nematodes. Parasitol Res. 105, 1239–1252 (2009).PubMed 

    Google Scholar 
    Cavallero, S., Nadler, S. A., Paggi, L., Barros, N. B. & D’Amelio, S. Molecular characterization and phylogeny of anisakid nematodes from cetaceans from southeastern Atlantic coasts of USA, Gulf of Mexico, and Caribbean Sea. Parasitol. Res. 108, 781–792 (2011).PubMed 

    Google Scholar 
    Kijewska, A., Dzido, J., Shukhgalter, O. & Rokicki, J. Anisakid parasites of fishes caught on the African shelf. J. Parasitol. 95, 639–645 (2009).PubMed 

    Google Scholar 
    Quiazon, K. M. A., Santos, M. D. & Yoshinaga, T. Anisakis species (nematoda: Anisakidae) of dwarf sperm whale kogia sima (Owen, 1866) stranded off the pacific coast of southern Philippine archipelago. Vet. Parasitol. https://doi.org/10.1016/J.VETPAR.2013.05.019 (2013).Article 
    PubMed 

    Google Scholar 
    Zhang, L., Du, X., An, R., Li, L. & Gasser, R. B. Identification and genetic characterization of Anisakis larvae from marine fishes in the South China Sea using an electrophoretic-guided approach. Electrophoresis 34, 888–894 (2013).CAS 
    PubMed 

    Google Scholar 
    Luo, H.-Y., Chen, H.-Y., Chen, H.-G. & Shih, H.-H. Scavenging hagfish as a transport host of anisakid nematodes. Vet. Parasitol. 218, 15–21. https://doi.org/10.1016/j.vetpar.2016.01.005 (2016).Article 
    PubMed 

    Google Scholar 
    Kuhn, T., Hailer, F., Palm, H. W. & Klimpel, S. Global assessment of molecularly identified Anisakis dujardin, 1845 (nematoda: Anisakidae) in their teleost intermediate hosts. Folia Parasitol. (Praha). 60, 123–134. https://doi.org/10.14411/fp.2013.013 (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Grainger, J. N. R. The Identity of the larval nematodes found in the body muscles of the cod (Gadus callarias L.). Parasitology 49, 121–131 (1959).CAS 
    PubMed 

    Google Scholar 
    Costa, G., Chada, T., Melo-Moreira, E., Cavallero, S. & D’Amelio, S. Endohelminth parasites of the leafscale gulper shark, Centrophorus squamosus (Bonnaterre, 1788) (Squaliformes: Centrophoridae) off Madeira archipelago. Acta Parasitol. 59, 316–322. https://doi.org/10.2478/s11686-014-0247-x (2014).Article 
    PubMed 

    Google Scholar 
    Hermida, M. et al. Infection levels and diversity of anisakid nematodes in blackspot seabream, Pagellus bogaraveo, from Portuguese waters. Parasitol. Res. 110, 1919–1928 (2012).PubMed 

    Google Scholar 
    Sequeira, V. et al. Macroparasites as biological tags for stock identification of the bluemouth, Helicolenus dactylopterus (Delaroche, 1809) in Portuguese waters. Fish Res. 106, 321–328. https://doi.org/10.1016/j.fishres.2010.08.014 (2010).Article 

    Google Scholar 
    Shamsi, S., Spröhnle-Barrera, C. & Shafaet, H. M. Occurrence of Anisakis spp. (Nematoda: Anisakidae) in a pygmy sperm whale Kogia breviceps (Cetacea: Kogiidae) in Australian waters. Dis. Aquat. Organ. 134, 65–74. https://doi.org/10.3354/dao03360 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Mcalpine, D. F., Murison, L. D. & Hoberg, E. P. New records for the pygmy sperm whale, Kogia breviceps (physeteridae) from Atlantic Canada with notes on diet and parasites. Mar. Mammal. Sci. 13, 701–704. https://doi.org/10.1111/j.1748-7692.1997.tb00093.x (1997).Article 

    Google Scholar 
    Gunter, G. & Overstreet, R. Cetacean notes. I. Sei and rorqual whales on the Mississippi coast, a correction. II. A dwarf sperm whale in Mississippi sound and its helminth parasites. Gulf Res. Rep. 4, 479–481 (1974).
    Google Scholar 
    Mignucci-Giannoni, A. A., Hoberg, E. P., Siegel-Causey, D. & Williams, E. H. Metazoan parasites and other symbionts of cetaceans in the Caribbean. J. Parasitol. 84, 939–946 (1998).CAS 
    PubMed 

    Google Scholar 
    Vidal, O., Findley, L. T., Turk, P. J. & Boyer, R. E. Recent records of pygmy sperm whales in the Gulf of California. Mexico. Mar. Mammal. Sci. 3, 354–356. https://doi.org/10.1111/J.1748-7692.1987.TB00323.X (1987).Article 

    Google Scholar 
    Dollfus, R. P. Helminthofaune de Kogia breviceps (Blainxille, 1938) cetace odontocete. Recoltes du Dr R. Duguy. Ann. Sci. Natl. Charente-Maritime 4, 3–6 (1966).
    Google Scholar 
    MCAlpine, D.F., (2018) Pygmy and dwarf sperm whales. In: Encyclopedia of Marine Mammals. Elsevier p. 786–8.Fernández, R., Santos, M. B., Carrillo, M., Tejedor, M. & Pierce, G. J. Stomach contents of cetaceans stranded in the canary Islands 1996–2006. J. Mar. Biol. Assoc. United Kingdom. 89, 873–883 (2009).

    Google Scholar 
    Berrow, S., López Suárez, P., Jann, B., Ryan, C., Varela, J., Hazevoet, C.J., (2015) Recent and noteworthy records of Cetacea from the Cape Verde Islands. www.scvz.org. Accessed 1 Mar 2021.Mattiucci, S., Nascetti, G., (2008) Chapter 2 advances and trends in the molecular systematics of anisakid nematodes, with implications for their evolutionary ecology and host-parasite co-evolutionary processes. Adv. Parasitol. 66 47 148Measures, L.N., (2014) Anisakiosis and pseudoterranovosis. Reston, Virginia; https://doi.org/10.3133/cir1393McClelland, G. The trouble with sealworms (Pseudoterranova decipiens species complex, nematoda): A review. Parasitology 2002(124 Suppl), S183-203 (2009).
    Google Scholar 
    Alt, K. G., Cunze, S., Kochmann, J. & Klimpel, S. Parasites of three closely related Antarctic fish species (teleostei: Nototheniinae) from Elephant Island. Acta Parasitol. https://doi.org/10.1007/s11686-021-00455-8 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    McClelland, G. Phocanema decipiens (Nematoda: Anisakinae): Experimental infections in marine copepods. Can. J. Zool. 60, 502–509. https://doi.org/10.1139/z82-075 (1982).Article 

    Google Scholar 
    Marcogliese, D. J. Review of experimental and natural invertebrate hosts of sealworm (Pseudoterranova decipiens) and its distribution and abundance in macroinvertebrates in eastern Canada. NAMMCO Sci. Publ. 3, 27–37 (2001).
    Google Scholar 
    West, K. L. et al. Diet of pygmy sperm whales (Kogia breviceps) in the Hawaiian Archipelago. Mar. Mammal. Sci. 25, 931–943. https://doi.org/10.1111/j.1748-7692.2009.00295.x (2009).Article 

    Google Scholar 
    Kleinertz, S., Damriyasa, I. M., Hagen, W., Theisen, S. & Palm, H. W. An environmental assessment of the parasite fauna of the reef-associated grouper Epinephelus areolatus from Indonesian waters. J. Helminthol. 88, 50–63 (2014).CAS 
    PubMed 

    Google Scholar 
    Nadler, S. A. et al. Molecular phylogenetics and diagnosis of Anisakis, Pseudoterranova, and Contracaecum from northern pacific marine mammals. J. Parasitol. 91, 1413–1429 (2005).CAS 
    PubMed 

    Google Scholar 
    Weitzel, T. et al. Human infections with Pseudoterranova cattani nematodes. Chile. Emerg. Infect. Dis. 21, 1874–1875 (2015).CAS 
    PubMed 

    Google Scholar 
    Arizono, N., Miura, T., Yamada, M., Tegoshi, T. & Onishi, K. Human infection with Pseudoterranova azarasi roundworm. Emerg. Infect. Dis. 17, 555–556. https://doi.org/10.3201/eid1703.101350 (2011).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kleinertz, S. et al. Gastrointestinal parasites of free-living Indo-Pacific bottlenose dolphins (Tursiops aduncus) in the Northern Red Sea. Egypt. Parasitol Res. 113, 1405–1415. https://doi.org/10.1007/s00436-014-3781-4 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Aco Alburqueque, R., Palomba, M., Santoro, M. & Mattiucci, S. Molecular identification of zoonotic parasites of the genus Anisakis (Nematoda: Anisakidae) from fish of the southeastern Pacific Ocean (off Peru coast). Pathogens. 9, 910. https://doi.org/10.3390/pathogens9110910 (2020).Article 
    CAS 
    PubMed Central 

    Google Scholar 
    Di Azevedo, M. I. N., Carvalho, V. L. & Iñiguez, A. M. Integrative taxonomy of anisakid nematodes in stranded cetaceans from Brazilian waters: An update on parasite’s hosts and geographical records. Parasitol. Res. 116, 3105–3116. https://doi.org/10.1007/s00436-017-5622-8 (2017).Article 
    PubMed 

    Google Scholar 
    Quiazon, K. M. A., Santos, M. D., Blatchley, D. D., Aguila, R. D. & Yoshinaga, T. Molecular and morphological identifications of Anisakis dujardin, 1845 (Nematoda: Anisakidae) from a rare deraniyagala’s beaked whale (Mesoplodon hotaula deraniyagala, 1963) and blainville’s beaked whale (Mesoplodon densirostris blainville, 1817) stranded. Philipp. J. Sci. 150, 823–835 (2021).
    Google Scholar 
    Bao, M. et al. Air-dried stockfish of Northeast Arctic cod do not carry viable anisakid nematodes. Food Cont. 116, 107322. https://doi.org/10.1016/j.foodcont.2020.107322 (2020).Article 
    CAS 

    Google Scholar 
    Liu, G. H. et al. Mitochondrial phylogenomics yields strongly supported hypotheses for ascaridomorph nematodes. Sci. Rep. https://doi.org/10.1038/srep39248 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hrabar, J. et al. Phylogeny and pathology of anisakids parasitizing stranded California sea lions (Zalophus californianus) in Southern California. Front Mar. Sci. https://doi.org/10.3389/fmars.2021.636626 (2021).Article 

    Google Scholar  More

  • in

    Adsorption characteristics and mechanisms of Cd2+ from aqueous solution by biochar derived from corn stover

    Thermogravimetric/differential thermogravimetry analyses of corn stoverThermogravimetric/Differential Thermogravimetry (TG/DTG) curves are shown in Fig. 2. The pyrolysis process of corn stover could be divided into three stages. The first stage was the dehydration stage, which occurred at approximately 55–125 °C, and the weight loss was mainly accounted for by water19. The second stage was the pyrolysis stage, which occurred at approximately 200–400 °C and mainly involved the decomposition of cellulose, hemicellulose and a small amount of lignin. This process involved the generation of CO and CO2 and the breaking of carbonaceous polymer bonds20. In addition, a shoulder peak in the range of 265 to 300 °C in the DTG diagram could be caused by side chain decomposition and glycosidic bond cleavage of xylan during the pyrolysis of corn stover21. The third stage was the carbonization stage, which occurred above 400 °C; this stage mainly involved the decomposition of lignin22,23. The carbonization process was relatively slow after 600 °C; this process was called the passive pyrolysis stage24. In general, the TG loss in the pyrolysis process of corn stover was mainly from the moisture in the biomass sample in the first stage. Hemicellulose and cellulose decomposition occurred in the second stage, and lignin decomposition occurred in the third stage25. In this experiment, the minimum pyrolysis temperature for the preparation of biochar was 400 °C. Therefore, the pyrolysis of biochar was relatively complete.Figure 2TG/DTG curves of corn stover.Full size imageCharacterization of biocharYield and specific surface area analysesThe yield and SBET are presented in Table 2. BC, BC-H and BC-OH represent the origin, acid-modified, and base-modified biochar, respectively. The yield of corn stover biochar exhibited a negative correlation with the temperature and decreased from 39.65 to 28.26% when the pyrolysis temperature increased from 400 to 700 °C. This phenomenon could have occurred due to the loss of more volatile substances and the thermal degradation of lignocellulose with increasing temperature, thus reducing the yield of biochar26,27. The SBET of the original biochar showed little difference below 700 °C but increased significantly at 700 °C. Combined with the SEM analysis (Fig. 3), at low temperatures, more ashes on the surface of biochar could block its pores so that the change in SBET was not obvious. At 700 °C, because the ash content significantly reduced and the pyrolysis was more sufficient, the pores of the biochar were more developed, and the SEBT significantly increased. The SBET of the acid/base-modified biochar increased with increasing temperature. The SBET of biochar was larger than that of the original biochar after acid and base modification at 400–600 °C. This phenomenon occurred because the porous structure of biochar was enhanced by acid and base modification28. Moreover, pickling removed most of the inorganic substances in biochar and reduced ash content, while alkali washing removed the tar on the surface of biochar to a certain extent29. However, at 700 °C, the SBET of biochar after acid/base modification was lower than that of the original biochar. Combined with the SEM (Fig. 4), the acid/base modification caused the nanopores of biochar to collapse into mesopores or macropores30. Therefore, the well-developed pore structure of the biochar prepared at 700 °C was destroyed by acid/base modification, resulting in a significant decrease in SBET.Table 2 Yield and SBET of different biochars.Full size tableFigure 3SEM (ZEISS) images of biochar at different pyrolysis temperatures: (a) C1, (b) C8, (c) C12, and (d) C16.Full size imageFigure 4SEM (OPTON) images of C16 biochar and its acid/base modification: (a) C16, (b) C16-H, and (c) C–OH.Full size imageScanning electron microscopy analysisThe C1, C8, C12 and C16 biochars had the highest Cd2+ removal rates at 400, 500, 600 and 700 °C, respectively. Therefore, these BCs were selected for SEM analysis. Figure 3 clearly showed that as the pyrolysis temperature increased from 400 to 700 °C, the pore structure of biochar became more developed, with a smaller pore size and more pores. Although there were numerous pores at 500 °C, the pores were not fully developed and were blocked inside. At 700 °C, the skeleton structure appeared, and the particle size of ash blocked in the pores decreased.By taking C16 biochar with the highest removal rate of Cd2+ as the research object, the changes in the biochar surface before and after modification were compared. C16-H and C16-OH represent acid-modified and base-modified biochar, respectively. After acid/base modification, the ash content on the surface of the biochar decreased, and the pore size increased (Fig. 4). Therefore, some skeleton structures could collapse after corrosion, which was consistent with the previous SBET results. Sun et al. discovered that citric acid-modified biochar would lead to micropore wall collapse and micropore loss, resulting in a reduction in SBET31. This finding was in agreement with the results of our study.Fourier transform infrared spectroscopy analysisThe FTIR spectra of biochar at different pyrolysis temperatures are presented in Fig. 5a.Figure 5FTIR spectra of corn stover biochar: (a) different pyrolysis temperatures and (b) different modification treatments.Full size imageAs the pyrolysis temperature increased from 300 to 700 °C, the absorption peak intensity showed a downwards trend. There was a remarkable decrease in features associated with stretch O–H (3400 cm−1)32. The vibration peaks of C–H (2924 cm−1) and C=O (1610 cm−1) decreased with increasing temperature, which could be due to the reduction in –CH2 and –CH3 groups of small molecules and the pyrolysis of C=O into gas or liquid byproducts at high temperatures33. In addition, the peak at 1435 cm−1 was identified as the vibration of C=C bonds belonging to the aromatic skeleton of biochar. A decrease in the absorbance peaks was found at 1115 cm−1, which corresponded to C–O–C bonds. The ratio of intensities for C=C/C=O (1550–1650 cm−1) and C–O–C (1115 cm−1) to the shoulder (1100–1200 cm−1) gradually decreased, and the loss of –OH at 3444 cm−1 indicated that the oxygen content in biochar reduced. The cellulose and wood components were dehydrated, and the degree of biochar condensation increased at higher temperatures. The bending vibration peaks of Ar–H at 856 and 877 cm−1 changed little at different temperatures, which showed that the aromatic rings were relatively stable below 700 °C34. Combined with the above analysis the condensation degree of biochar increased gradually above 400 °C35,36. In summary, as the pyrolysis temperature increased, the degree of aromatization of biochar improved, and the numbers of oxygen-containing functional groups decreased continuously.Figure 5b showed that after acid/base modification, the absorbance peaks at 3444 cm−1, 1610 cm−1 and 1115 cm−1 increased, indicating that the number of oxygen-containing functional groups increased. However, the stretching vibration peak of aromatic ring skeleton C=C (1435 cm−1) and the bending vibration peaks of Ar–H (856–877 cm−1) changed little. The number of functional groups of acid-modified biochar increased more than that of alkali-modified biochar. Mahdi et al. found that acid modification increased the number of functional groups in a study of biochar modification37. After acid/base modification, the number of oxygen-containing functional groups, such as hydroxyl and carboxyl groups, increased.Optimization of biocharFigure 6 illustrates that the removal rates of Cd2+ by corn stover biochar (original, acid-modified, and base-modified biochars) consistently increased with increasing pyrolysis temperature. The highest removal rate reached 95.79% at 700 °C. The removal rate decreased after modification, especially after pickling. The results showed that C16 biochar had the best removal effect on Cd2+.Figure 6Cd2+ removal rate of different biochars (BC: original biochar, BC-OH: alkali-modified biochar, and BC-H: acid-modified biochar).Full size imageIntuitive and variance analyses were employed to explore the influences of biochar preparation conditions on the removal rate of Cd2+.

    1.

    Intuitive analysis
    The intuitive analysis of the orthogonal experiment is shown in Table 3 and Fig. 7. The pyrolysis temperature had the most significant influence on the removal of Cd2+, followed by the retention time and finally the heating rate. Therefore, the optimal conditions for biochar preparation were a pyrolysis temperature of 700 °C, a retention time of 2.5 h, and a heating rate of 5 °C/min.

    2.

    Variance analysis
    Variance analysis showed that the effect of pyrolysis temperature on the removal rate of Cd2+ was very significant (Table 4). The effects of retention time and heating rate were not significant. This phenomenon was consistent with the conclusions obtained in the intuitive analysis.

    Table 3 Intuitive analyses of influencing factors of biochar preparation.Full size tableFigure 7Intuitive analysis diagram of influencing factors for biochar preparation.Full size imageTable 4 Variance analysis.Full size tableAnalysis of adsorption mechanismThe SBET of the unmodified biochar did not change significantly with temperature, which indicated that SBET could potentially not be a critical factor for Cd2+ adsorption. Qi et al. obtained a similar conclusion when studying the adsorption of Cd2+ in water by chicken litter biochar38. In addition to SBET, the four primary mechanisms involved in the removal of heavy metal ions by biochar were as follows: (1) Ion exchange: the alkali or alkaline earth metals in biochar (K+, Ca2 +, Na+, and Mg2+) were the dominant cations in ion exchange39. (2) The complexation of oxygen-containing functional groups mainly included hydroxyl and carboxyl groups40. (3) Mineral precipitation: Cd2+ was precipitated by minerals on the surface of biochar to form Cd3(PO4)2 and CdCO341. Soluble cadmium precipitated with some anions released by biochar, such as CO32−, PO43− and OH−42,43. (4) π electron interaction: Cd2+ coordinated with the π electrons of C=C or C=O at low pyrolysis temperatures43,44. Biochar contains more aromatic structures at high pyrolysis temperatures, which could provide more π electrons. Therefore, the π electron interaction in adsorption of Cd2+ was effectively enhanced45.C1, C8, C12 and C16 were selected to study the adsorption mechanism. Related physicochemical properties are given in Table 5.Table 5 Physicochemical properties of biochar at different pyrolysis temperatures.Full size tableThe CEC of biochar gradually increased as the pyrolysis temperature increased, reaching a maximum at 600 °C and slightly decreasing at 700 °C. This phenomenon could have occurred because the crystalline minerals under high pyrolysis temperatures inhibited the exchange of cations on the surface of biochar with Cd2+ in aqueous solution46. Nevertheless, CEC did not change significantly with temperature; thus, CEC was not the main adsorption mechanism. With increasing pyrolysis temperature, the number of acidic functional groups decreased gradually, while the number of alkaline functional groups increased. The main functional groups used to remove Cd2+ were generally considered acidic oxygen-containing functional groups. However, the number of these functional groups decreased with increasing pyrolysis temperature, which weakened the complexation on the surface of the biochar. However, this result was contradictory to the results of Cd2+ adsorption. Therefore, the functional groups were not the main adsorption mechanism.To further explore the adsorption mechanism of Cd2+, the biochar before and after the adsorption of Cd2+ was characterized by XRD. As shown in Fig. 7a, C16-100Cd and C16-200Cd represented the biochar after Cd2+ adsorption when the concentrations of cadmium solution were 100 mg/l and 200 mg/l, respectively. The results showed that new peaks appeared at 30.275° and 36.546° after adsorption, corresponding to CdCO3. The spike at 29.454° was due to Cd(OH)2. Additionally, the intensity of the CdCO3 peak increased significantly from C16-100Cd to C16-200Cd, indicating that mineral precipitation occurred in adsorption. Liu et al. found similar results in a study on removing Cd2+ from water by blue algae biochar12. However, as the concentration of Cd2+ increased from 0 to 200 mg/L, the diffraction peak at 2θ = 29.454° first increased and then decreased. This because the peak position of CaCO3 at 2θ = 29.369° was very close to Cd(OH)2 at 2θ = 29.454°. At low concentrations, the production of Cd(OH)2 was greater than that of CdCO3. When the initial concentration of Cd2+ increased, more CO32− released by CaCO3 combined with Cd2+ to form CdCO3, resulting in a reduction in the diffraction peak.As presented in Fig. 8b, the peak intensities of CdCO3 and Cd(OH)2 gradually increase with increasing pyrolysis temperature. On the one hand, this phenomenon could be ascribed to the increase in the mineral content of biochar with increasing pyrolysis temperature. On the other hand, the pH value of biochar increased with increasing pyrolysis temperature. In this way, more OH− was released, thus forming more Cd(OH)2. Wang et al. obtained similar results42. Moreover, the peak intensity of KCl at 2θ = 28.347° decreased after adsorption, as shown in Fig. 8a, which indicated that ion exchange took part in adsorption.Figure 8XRD images: (a) before and after adsorption of Cd2+ on C16 biochar and (b) Cd2+ adsorption by biochar at different pyrolysis temperatures.Full size imageIn addition, the FTIR spectra showed that the number of functional groups, such as C=C and C=O, in biochar decreased with increasing pyrolysis temperature, leading to the weakening of cation–π interactions between Cd2+ and C=C and C=O. In contrast, due to the enhanced aromatization of functional groups on the surface of biochar, many lone pair electrons existed in the electron-rich domains of the graphene-like structure, which in turn enhanced the cation–π interactions. Harvey et al., based on the study of Cd2+ adsorption by plant biochar, concluded that the electron-rich domain bonding mechanism between Cd2+ and the graphene-like structure on the surface of biochar played a more significant role in biochar with a high degree of carbonization45. Therefore, π-electron interactions could play a dominant role in Cd2+ adsorption on high-temperature pyrolysis biochar. Moreover, the results showed that the number of alkaline functional groups increased while acidic functional groups decreased with the increase in pyrolysis temperature. It is generally believed that acidic functional groups could withdraw electrons, and basic functional groups could donate electrons47,48. The biochar with higher pyrolysis temperature contained more alkaline functional groups, which improved the electron donating ability of biochar and enhanced the cation–π electron effect.In summary, mineral precipitation and π electron coordination were the main mechanisms of removing Cd2+ from water by corn stover biochar. This phenomenon explained why the Cd2+ removal rate of acid/base–modified biochar decreased. After modification, the functional groups on the surface of biochar increased, but the inorganic minerals were removed. Pickling resulted in the loss of soluble minerals and alkaline functional groups on the surface of biochar, which was not conducive to adsorption49. After alkaline washing, more PO43−, CO32− and HCO3− were released, thereby reducing the mineral precipitation50,51. Since NaOH had a weaker destructive effect than HCl and introduced some OH−, alkaline washing had little effect on the removal rate of Cd2+.Adsorption isotherm and adsorption kineticsAdsorption isothermThe adsorption isotherms were fitted with Langmuir (Eq. 3) and Freundlich (Eq. 4) models, as shown in Fig. 9, and the fitting parameters are listed in Table 6.Figure 9Adsorption isotherm.Full size imageTable 6 Fitting parameters of the adsorption isotherm model.Full size tableThe Langmuir model (R2  > 0.963) was more suitable than the Freundlich model (R2  > 0.919), indicating that the adsorption sites of biochar were evenly distributed, and adsorption was mainly monolayer. Parameter KL reflected the difficulty of adsorption and was generally divided into four types: unfavourable (KL  > 1), favourable (0  More

  • in

    Mapping tropical forest functional variation at satellite remote sensing resolutions depends on key traits

    We hypothesized that functionally distinct forest types can be mapped at moderate spatial resolutions, using a combination of canopy foliar traits and canopy structure information. Our analysis of LiDAR and imaging spectroscopy data at spatial resolutions ranging from 4 to 200 m (16 m2–40,000 m2), with an emphasis on the 30 m (900 m2) spaceborne hyperspectral spatial resolution, reveals that few remotely sensed canopy properties are needed to successfully identify ecologically distinct forest types at two diverse tropical forest sites in Malaysian Borneo. In testing our second hypothesis that mapped forest types exhibit distinct ecosystem function, we found that forest types identified using remotely sensed leaf P, LMA, Max H, and canopy cover at 20 m height (Cover20) closely align with forest types defined from field-based floristic surveys29,30,31,32,33 and inventory plot-based measurements of growth and mortality rates (Fig. 4b). Our approach, however, enables mapping of their entire spatial extent (Fig. 1) and reveals important structural and functional variation within areas characterized as a single forest type in previous studies (Fig. 3). Current and forthcoming satellite hyperspectral platforms, including PRISMA (30 m), CHIME (20–30 m), and SBG (30 m), have or will have comparable spectral resolution, higher temporal revisits, and much greater geographic coverage. The ability to conduct this type of analysis using remote sensing measurements at 30 m resolution suggests that our method can be applied to these emerging spaceborne imaging spectroscopy data to reveal important differences in structure and function across the world’s tropical forests.Nested functional forest types revealedTo test our first hypothesis, rather than making an a priori decision about the number of k-means clusters (k), we explored the capacity of remotely sensed data to reveal ecologically relevant variation in forest types. Baldeck and Asner took a similar unsupervised approach to estimating beta diversity in South Africa34. Because the choice of k directly influences analysis outcomes, careful selection of k is required. Different approaches for identifying the number of clusters, using the Gapk and Wk elbow metrics35, yielded varying optimal numbers of clusters for the Sepilok and Danum landscapes (Fig. 1, Supplementary Figs. 4 and 5). However, at both sites, a comparison of results based on different values of k revealed ecologically meaningful structural and functional differences and graduated transitions between forest types (Fig. 2, Supplementary Figs. 7 and 8), indicating that the exploration of traits that aggregate or separate forest types as k changes is a valuable exercise. Overlap between the remotely sensed forest type boundaries and inventory plots within distinct forest types indicate that the series of clustered forests align closely with forest types defined based on in situ data on species composition and ecosystem structure. In part, this type of analysis requires careful selection of the number of clusters. Additionally, however, we gained valuable insights via the exploration of varying numbers of clusters as it relates to biologically meaningful categorization of forest types. Extending this method to other parts of the tropics will require similar decision-making, which will either require user input, or the development of robust automated algorithms for selecting k.Forest types capture differences in ecosystem dynamicsWe further evaluated the canopy traits and structural attributes that were most critical for mapping distinct forest types, hypothesizing that mapped forest types exhibit distinct ecosystem function. Forest types revealed by the cluster analyses were distributed along the leaf economic spectrum, where the leaf economic spectrum characterizes a tradeoff in plant growth strategies36. LMA, which can covary strongly with leaf N and P, is a key indicator of plant growth strategies along the spectrum37. At the slow-return end of the leaf economics spectrum, plants in nutrient-poor conditions with low leaf nutrient concentrations invest in leaf structure and defense, expressed as high LMA, strategizing longer-lived, tougher leaves with slower decomposition rates. This strategy comes at the cost of slower growth. At the quick-return end of the spectrum, plants in nutrient-rich environments with higher leaf nutrient concentrations invest less in structure and defense, enabling faster growth and more rapid leaf turnover, i.e., shorter leaf lifespans. This quick-return growth strategy supports higher photosynthetic rates and more rapid carbon gain36.In this study, the principal components and clustering results yielded forest types that are indicative of community level differences associated with leaf economic spectrum differences. The nutrient rich sites (Danum1 and Danum2, Supplementary Fig. 8) show high canopy N and P and low LMA compared to the nutrient poor and acidic sites (Sandstone and Kerangas), which contributes to lower leaf photosynthetic capacity (Vcmax) and growth (Fig. 4b). Foliar N:P also increased with site fertility, confirming that tropical forests are primarily limited by phosphorus, and not nitrogen38,39, with large implications for carbon sequestration in these forests. Orthogonal differences in canopy structure and architecture between Danum forest types and Sepilok Sandstone and Alluvial forests could be indicative of ecosystem scale differences in the sensitivity of these forests to endogenous disturbance processes40.The significant differences in aboveground carbon stocks and growth and mortality rates between forest types further suggests strong differences in ecosystem dynamics. In general, growth rates varied inversely to aboveground carbon, and higher aboveground carbon corresponded to lower mortality rates. As an example, the Sepilok sandstone forests, which are largely comprised of slow-growing dipterocarp species29,33, had the highest median aboveground carbon (236 Mg C ha−1), with higher canopy P and N, and lower LMA. The taller canopy and low canopy leaf nutrient concentrations are consistent with the low growth and mortality rates found in the sandstone forest, indicating a slow-growth strategy yielding larger trees and higher aboveground carbon stocks. In contrast, alluvial forests exhibit high turnover with mortality and growth rates higher relative to Sandstone forests corresponding to lower aboveground carbon on average. Kerangas forests exhibited low aboveground carbon despite an intermediate plot-level growth rate, and mortality rates that were significantly lower than the Danum or alluvial forest types. Kerangas forests, which were characterized by the highest LMA, lowest foliar P and N (Fig. 2a), and the lowest plot-level aboveground carbon density (186 Mg C ha−1; Fig. 4a), are known to have higher stem densities, lower canopy heights, and long-lived leaves5,32,41, suggesting well-developed strategies for nutrient retention42. Interestingly, despite significantly different aboveground carbon and demography, the kerangas and sandstone forests did not differ in LAI or canopy architecture (P:H); although maximum height, Cover20, and Hpeak LAI were significantly higher in the sandstone forest, highlighting the need to account for differences beyond LAI when scaling processes from leaves to ecosystems.In addition, when three forest types were distinguished at Sepilok, the alluvial inventory plot had significantly higher aboveground carbon than the remote sensing-derived alluvial forest extent (Fig. 4a, p  More

  • in

    Multi-species occupancy modeling suggests interspecific interaction among the three ungulate species

    Study areaThe present study was conducted in Uttarkashi district, Uttarakhand, located between 38° 28′ to 31°28′ N latitude and 77°49′ to 79°25′ E longitude with an area of about 8016 km2, covering primarily hilly terrain with an altitudinal range of 715–6717 m (Fig. 3). The terrain is mountainous, consisting of undulating hill ranges and narrow valleys with temperate climatic conditions. The district lies in the upper catchment of two major rivers of India, viz., the Ganges (Bhagirathi towards upstream) and the Yamuna. The major vegetation types of the study area are Himalayan moist temperate forest, sub-alpine forest and alpine scrub59. The Uttarkashi district forests are managed under three Forest Divisions viz., (i) Uttarkashi Forest Division (ii) Upper Yamuna Badkot Forest Division and (iii) Tons Forest Division) with two Protected Areas (PAs) (i) Gangotri National Park and (ii) Govind Pashu Vihar National Park. The forested habitats of the study landscape are home to top conservation priority species, including Asiatic Black bear (Ursus thibetanus), Musk deer (Moschus spp.), Common leopard (Panthera pardus), Himalayan brown bear (Ursus arctos isabellinus) and Western Tragopan (Tragopan melanocephalus), Himalayan monal (Lophophorus impejanus). The study was conducted after a study permit issued by the Chief Wildlife Warden, Forest Department, Uttarakhand government, vide letter no. 848/5-6 dated 31/08/2019, we have not handled the species for doing research. Instead, remote camera traps have been used for collecting the data with the permission of the Chief Wildlife Warden, Government of Uttarakhand. Further, informed consent was taken before interviewing the local communities. The data was collected according to the institutional guidelines and approved by the Research Advisory and Monitoring Committee of the Zoological Survey of India.Figure 3Map of the study area Uttarkashi, Uttarakhand. ArcGIS 10.6 (ESRI, Redlands, CA) was used to create the map. (Map created using ArcGIS 10.6; http://www.esri.com).Full size imageSampling protocolThe basic sampling protocol and assumptions for multi-species occupancy modelling are identical to the single-species case7. Briefly, a set of 62 intensive sites, were randomly selected, and each site i was surveyed j times. During each survey, detection/non-detection of S focal species was recorded. Additionally, direct or indirect evidences of species presence from the different areas were also recorded.Data collectionThe complete study area was divided into 10 × 10 km grids, consisting of n = 60 grids. Based on the reconnaissance survey, out of these 60 grids, we selected 25 girds that were accessible to conduct the survey and have the species presence. Further, these grids were divided into 2 × 2 km grids to maximize our effort so that all logistically accessible grids could be covered, and we conducted intensive sampling in N = 62 grids after excluding the grids with human settlements. T The field surveys were conducted during 2018–2019, and a team of researchers systematically visited selected grids to collect data on the detection/non-detection of these ungulates. A total of 62 camera traps were deployed in selected grids, and 650 km were traversed, accounting for N = 54 trails in these sampled grids. These camera traps were visited once in every fifteen days for replacing the batteries as well as documenting the presence of the species through the sign surveys. The ultra-compact SPYPOINT FORCE-11D trail camera (SPYPOINT, GG Telecom, Canada, QC) and Browning trail camera (Defender 850, 20 MP, Prometheus Group, LLC Birmingham, Alabama, https://browningtrailcameras.com) camera traps were used to detect the presence/absence of ungulate species. The cameras were mounted 40–60 cm above ground on natural trails without lures.Data explorationWhile deploying camera traps, we also noted habitat variables through on-site observation such as distance to the village and human disturbance. We tested site covariates for collinearity and discarded one of a pair if the Pearson’s correlation was greater than 0.760. Hence, we assumed each of the site covariates could influence the occupancy and detectability of these ungulates.CovariatesWe hypothesized that habitat variables may influence these ungulates’ occupancy and detection probability. A total of 21 variables were extracted either from the field or using the ArcGIS v. 10.6 software (ESRI, Redlands, CA), and only 14 were retained after collinearity testing60 (Table 3). These covariates were classified into the following categories (Topographic variables, Habitat variables and anthropogenic variables). The topographic variables (elevation, slope and aspect) were generated using 30× resolution SRTM (Shuttle Radar Topography Mission) image downloaded from EarthExplorer (https://earthexplorer.usgs.gov/). The habitat/ land cover classification was carried out using Landsat 8 satellite imagery (Spatial resolution = 30 m) downloaded from Global Land Cover Facility by following the methodology suggested by61 using the ArcGIS v. 10.6 software (ESRI, Redlands, CA). The study area was classified into nine Land use/land cover (LULC) classes viz., West Himalayan Sub-alpine birch/fir Forest (FT 188), West Himalayan upper oak/fir forest (FT 162), West Himalayan Dry juniper forest (FT 180), Ban oak forest (FT 152), Moist Deodar Forest (FT 155), Western mixed coniferous forest (FT 156), Moist temperate Deciduous Forest (FT 157) which were used for further analysis considering their importance to species ecology and behavior60. The values for all the covariates were extracted at 30 m resolution, and a single value per site was obtained by averaging all the pixel values within each sampling site (camera trap locations).Table 3 Habitat variables used for multi species occupancy analysis of three ungulate species in Uttarkashi, Uttarakhand.Full size tableOccupancy modelling frameworkWe used multi-species occupancy modelling62 of barking deer, goral and sambar to estimate the probability of the species (s) occurred within the area (i) sampled during our survey period (j), for accounting the imperfect detection of the species8. Distinguishing the true presence/absence of a species from detection/non-detection (i.e., species present and captured or species present but not captured) requires spatially or temporally replicated data. We used camera stations to record the presence/absence of species along with sign survey in all the studied grids. The camera traps were placed along the trail/transects in the studied grids hence each grid needs to be visited once in every fifteen days to check the camera traps as well as to document the presence of the studied species. Therefore, we treated 15 trap nights as one sampling occasion at a particular camera station resulting in ~ 7 sampling occasions per camera station.Our aim was to record the presence/ absence of the species at a particular gird hence we incorporated sign survey data if the species was not detected in camera station but recorded through sign survey. We pooled the presence/absence data in a single sheet of each species following6 and fitted occupancy and detectability models using programme Mark63,64. We model the species (s) presence (ysij = 1) and absence (ysij = 0) at site i during survey j, and the sampling protocol was identical to single species case65, where the Bernoulli random variable was conditional on the presence of species s (Zs = 1) following6$${text{y}}_{sij} sim {text{ Bernoulli}}left( {{text{p}}_{sij} {text{z}}_{si} } right),$$
    where Psij represents the probability of detecting species S during replicate survey j at site i and Zsi = presence or absence of species s at site i.Furthermore, we model the latent occupancy state of species s at site i as a multivariate Bernoulli random variable:$${text{Z}}_{i} sim {text{MVB}}left( {uppsi _{i} } right)$$
    where Zi = {Z1i, Z2i….., ZSi} is an S-dimensional vector of 1’s and 0’s denoting the latent occupancy state of all S species and (ψi) is a 2S-dimensional vector denoting the probability of all possible sequences of 1’s and 0’s Zi can attain such that ∑ ψi = 1 with corresponding probability mass function (PMF) adopted from6,64.$$fleft( {{text{Z}}_{i} } right) = {text{ exp}}left( {left( {{text{Z}}_{i} {text{log}}(uppsi_{{text{i}}} {1}/uppsi_{{text{i}}} 0} right) , + {text{ log}}left( {uppsi_{{text{i}}} 0} right)} right).$$The quantity f = log (ψi1/ψi0), is the log odds species S occupies a site often referred to as a ‘natural parameter’.Since we are modeling three ungulate species (S = 3), 2S = 23 the possible encounter histories included in the dataset were eight, if neither of the two species were detected the value of ‘00’ was assigned; similarly ‘01’ indicates detection of species 1; ‘02’ indicates detection of species 2; ‘03’ indicates detection of both the species; ‘04’ indicates detection of species 3; ‘05’ indicates detection of species 1 and species 3; ‘06’ indicates detection of species 2 and species 3 and ‘07’ indicates detection of all the three species. We modelled constant occupancy and detection probability for each of the three species. Hence, we specified 6 f and p parameters, an intercept (β) for each of one-way f parameter and detection parameter p following64.$$f_{{1}}=upbeta_{{{1},}} ;;{text{p}}=upbeta_{{4}}$$$$f_{{2}} = upbeta_{{{2},}} ;{text{p }} = , upbeta 5$$$$f_{{3}} = , upbeta_{{{3},}}; {text{p }} = , upbeta_{{6}}$$We fit a set of models including the detection probability as a constant, p(.), and variable function to occupancy ψ(covariate) for site-specific covariates and models include occupancy as constant ψ(.) and variable function of the detection p(covariates) for the respective site covariates.As we have assumed the independence among all three species, the model shows marginal occupancy probabilities of species 1, species 2 and species 3 varies as a function of environmental variables. We incorporated site-level characteristics affecting species-specific occurrence (f1: occupancy of species 1, f2: occupancy of species 2, & f3: occupancy of species 3) and detection probabilities using a generalized linear modelling approach42. This requires 9 parameters: an intercept (β1, β3, β5) and slope (β2, β4, β6) coefficient for each 1-way f parameter f1, f2, f3 and an intercept parameter for each detection parameter (β7, β8, β9). Below mentioned is the model for 1-way f parameters.$$f_{{1}} = , upbeta_{{{1 } + }} upbeta_{{2}} left( {{text{Covariate}}} right),;;{text{ p }} = , upbeta_{{7}}$$$$f_{{2}} = , upbeta_{{{3 } + }} upbeta_{{4}} left( {{text{Covariate}}} right),;;{text{ p}} = , upbeta_{{8}}$$$$f_{{3}} = , upbeta_{{5}} + , upbeta_{{6}} left( {{text{Covariate}}} right),;;{text{ p }} = , upbeta_{{9}} .$$All covariates were standardized before model fitting. We fitted the most complex model to each species and considered all possible combinations of covariates using the logit link function. Our rationale for including these variables in the occupancy and detectability component of the model was that we expected these variables to influence the occupancy and detectability of the study species.Since multi-species occupancy simultaneously model environmental variables, & interspecific interactions. Further it also allows to understand the influence of environmental variables on one species occupancy, in the presence or absence of other sympatric species64. Hence, we also modeled two species occur together as a function of covariates. We examined how the variables of each camera site influenced the pair-wise interaction of the three ungulate species. This model assumes that the conditional probability of one species varies in the presence or absence of other species. We assumed f123: co-occurrence of species 1, species 2 & species 3 = 0, hence we did not include higher-order interactions in any of our models, we assumed the conditional probability of 3 species occurred together was purely a function of species-specific (f1, f2, f3) and pair-wise interaction (f12: co-occurrence of species1 & species 2, f13: co-occurrence of species 1 & species 3, f23: co-occurrence of species 2 & species 3) parameters. We modeled pair-wise interaction of species varies as a function of environmental variables keeping detection probability constant. Hence, we specified 15 f and p parameters, an intercept and slope coefficient for each of the one-way (f1, f2, f3) and the two-way f parameters (f12, f13, and f23); as well as an intercept parameter for each of the detection models. The model equation below implies for 2-way f parameters:$$f_{{{12}}} = , upbeta_{{{7 } + }} upbeta_{{8}} left( {{text{Covariate}}} right),;;{text{ p }} = , upbeta_{{{13}}}$$$$f_{{{13}}} = , upbeta_{{{9 } + }} upbeta_{{{1}0}} left( {{text{Covariate}}} right),;;{text{ p }} = , upbeta_{{{14}}}$$$$f_{{{23}}} = , upbeta_{{{11 } + }} upbeta_{{{12}}} left( {{text{Covariate}}} right),;;{text{ p }} = , upbeta_{{{15}}} .$$We also fitted models including co-occurrence and detection probability of a species varies as a function of environmental variables. Hence, we specified 18 f and p parameters, an intercept and slope coefficient for each of one-way (f1, f2, f3) and two-way f parameters (f12, f13, f23); and an intercept as well as the slope parameters for each of the detection models. The model equation below implies for 2-way f parameters:$$f_{{{12}}} = , upbeta_{{{7 } + }} upbeta_{{8}} left( {{text{Covariate}}} right),{text{ p }} = , upbeta_{{{13 } + }} upbeta_{{{14}}} left( {{text{covariate}}} right)$$$$f_{{{13}}} = , upbeta_{{{9 } + }} upbeta_{{{1}0}} left( {{text{Covariate}}} right),{text{ p }} = , upbeta_{{{15}}} + , upbeta_{{{16}}} left( {{text{covariate}}} right)$$$$f_{{{23}}} = , upbeta_{{{11 } + }} upbeta_{{{12}}} left( {{text{Covariate}}} right),{text{ p }} = , upbeta_{{{17}}} + , upbeta_{{{18}}} left( {{text{covariate}}} right)$$A total of 38 models were run to test the influence of environmental variables on occupancy and detection probability of species-specific (f1, f2, f3) and pair-wise interaction of the three ungulate species. The best-supported model was identified by selecting the model with the lowest AICc value and highest model weights66, where higher model weights indicate a better fit of the model to the data. Second-Order Information Criterion (AICc)67 values were used to rank the occupancy models, and all the models whose ΔAICc  More

  • in

    Citizen science plant observations encode global trait patterns

    Sakschewski, B. et al. Leaf and stem economics spectra drive diversity of functional plant traits in a dynamic global vegetation model. Glob. Change Biol. 21, 2711–2725 (2015).Article 

    Google Scholar 
    Berzaghi, F. et al. Towards a new generation of trait-flexible vegetation models. Trends Ecol. Evol. 35, 191–205 (2020).Article 
    PubMed 

    Google Scholar 
    Bruelheide, H. et al. Global trait–environment relationships of plant communities. Nat. Ecol. Evol. 2, 1906–1917 (2018).Article 
    PubMed 

    Google Scholar 
    Joswig, J. S. et al. Climatic and soil factors explain the two-dimensional spectrum of global plant trait variation. Nat. Ecol. Evol. 6, 36–50 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    van Bodegom, P. M., Douma, J. C. & Verheijen, L. M. A fully traits-based approach to modeling global vegetation distribution. Proc. Natl Acad. Sci. USA 111, 13733–13738 (2014).PubMed Central 

    Google Scholar 
    Moreno Martínez, A. et al. A methodology to derive global maps of leaf traits using remote sensing and climate data. Remote Sens. Environ. 218, 69–88 (2018).Article 

    Google Scholar 
    Pérez-Harguindeguy, N. et al. New handbook for standardized measurment of plant functional traits worldwide. Aust. J. Bot. 23, 167–234 (2013).Article 

    Google Scholar 
    Kattge, J. et al. TRY—a global database of plant traits. Glob. Change Biol. 17, 2905–2935 (2011).Article 

    Google Scholar 
    Kattge, J. et al. TRY plant trait database-enhanced coverage and open access. Glob. Change Biol. 26, 119–188 (2020).Article 

    Google Scholar 
    Jetz, W. et al. Monitoring plant functional diversity from space. Nat. Plants 2, 16024 (2016).Article 
    PubMed 

    Google Scholar 
    Butler, E. E. et al. Mapping local and global variability in plant trait distributions. Proc. Natl Acad. Sci. USA 114, E10937–E10946 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Boonman, C. C. et al. Assessing the reliability of predicted plant trait distributions at the global scale. Glob. Ecol. Biogeogr. 29, 1034–1051 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Madani, N. et al. Future global productivity will be affected by plant trait response to climate. Sci. Rep. 8, 2870 (2018).Vallicrosa, H. et al. Global distribution and drivers of forest biome foliar nitrogen to phosphorus ratios (N:P). Glob. Ecol. Biogeogr. 31, 861–871 (2022).Article 

    Google Scholar 
    Meyer, H. & Pebesma, E. Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods Ecol. Evol. 12, 1620–1633 (2021).Article 

    Google Scholar 
    Schiller, C. et al. Deep learning and citizen science enable automated plant trait predictions from photographs. Sci. Rep. 11, 16395 (2021).Aguirre-Gutiérrez, J. et al. Pantropical modelling of canopy functional traits using sentinel-2 remote sensing data. Remote Sens. Environ. 252, 112–122 (2021).Article 

    Google Scholar 
    Homolova, L. et al. Review of optical-based remote sensing for plant trait mapping. Ecol. Complex. 15, 1–16 (2013).Article 

    Google Scholar 
    Van Cleemput, E. et al. The functional characterization of grass-and-shrubland ecosystems using hyperspectral remote sensing: trends, accuracy and moderating variables. Remote Sens. Environ. 209, 747–763 (2018).Article 

    Google Scholar 
    Kattenborn, T., Fassnacht, F. E. & Schmidtlein, S. Differentiating plant functional types using reflectance: which traits make the difference? Remote Sens. Ecol. Conserv. 5, 5–19 (2019).Article 

    Google Scholar 
    Hauser, L. T. et al. Explaining discrepancies between spectral and in-situ plant diversity in multispectral satellite earth observation. Remote Sens. Environ. 265, 112684 (2021).Article 

    Google Scholar 
    Wäldchen, J. & Mäder, P. Plant species identification using computer vision techniques: a systematic literature review. Arch. Comput. Methods Eng. 25, 507–543 (2018).Article 
    PubMed 

    Google Scholar 
    Jones, H. G. What plant is that? Tests of automated image recognition apps for plant identification on plants from the British flora. AoB Plants 12, plaa052 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hampton, S. E. et al. Big data and the future of ecology. Front. Ecol. Environ. 11, 156–162 (2013).Article 

    Google Scholar 
    WÜest, R. O. et al. Macroecology in the age of big data—where to go from here? J. Biogeogr. 47, 1–12 (2020).Article 

    Google Scholar 
    Mäder, P. et al. The Flora Incognita app—interactive plant species identification. Methods Ecol. Evol. 12, 1335–1342 (2021).Article 

    Google Scholar 
    Di Cecco, G. J. et al. Observing the observers: how participants contribute data to iNaturalist and implications for biodiversity science. BioScience 71, 1179–1188 (2021).Article 

    Google Scholar 
    Mahecha, M. D. et al. Crowd-sourced plant occurrence data provide a reliable description of macroecological gradients. Ecography 44, 1131–1142 (2021).Article 

    Google Scholar 
    Botella, C. et al. Jointly estimating spatial sampling effort and habitat suitability for multiple species from opportunistic presence-only data. Methods Ecol. Evol. 12, 933–945 (2021).Article 

    Google Scholar 
    iNaturalist Research-Grade Observations (GBIF, accessed 5 January 2022); https://www.gbif.org/dataset/50c9509d-22c7-4a22-a47d-8c48425ef4a7Callaghan, C. T. et al. Three frontiers for the future of biodiversity research using citizen science data. BioScience 71, 55–63 (2020).
    Google Scholar 
    Dickinson, J. L., Zuckerberg, B. & Bonter, D. N. Citizen science as an ecological research tool: challenges and benefits. Ann. Rev. Ecol. Evol. Syst. 41, 149–172 (2010).Article 

    Google Scholar 
    Kosmala, M. et al. Assessing data quality in citizen science. Front. Ecol. Environ. 14, 551–560 (2016).Article 

    Google Scholar 
    Boakes, E. H. et al. Patterns of contribution to citizen science biodiversity projects increase understanding of volunteers’ recording behaviour. Sci. Rep. 6, 33051 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bowler, D.E. et al. Temporal trends in the spatial bias of species occurrence records. Ecography 2022, e06219 (2022). https://doi.org/10.1111/ecog.06219GBIF Occurrence Download (GBIF, 4 January 2022); https://doi.org/10.15468/dl.34tjreBruelheide, H. et al. sPlot—a new tool for global vegetation analyses. journal of vegetation science. J. Veg. Sci. 30, 161–186 (2019).Article 

    Google Scholar 
    Sabatini, F. et al. sPlotOpen—an environmentally balanced, open access, global dataset of vegetation plots. Glob. Ecol. Biogeogr. 30, 1740–1764 (2021).Article 

    Google Scholar 
    Whittaker, R.H. et al. Communities and Ecosystems (Macmillan/Collier Macmillan, 1970).Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51, 933–938 (2001).Article 

    Google Scholar 
    Joswig, J., Wirth, C. & Schuman, M. Climatic and soil factors explain the two-dimensional spectrum of global plant trait variation. Nat. Ecol. Evol. 6, 36–50 (2022).Article 
    PubMed 

    Google Scholar 
    Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).Article 
    PubMed 

    Google Scholar 
    Ploton, P. et al. Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nat. Commun. 11, 4540 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Meyer, H. & Pebesma, E. Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Methods Ecol. Evol. 12, 1620–1633 (2021).Article 

    Google Scholar 
    Schrodt, F. et al. Bhpmf—a hierarchical Bayesian approach to gap filling and trait prediction for macroecology and functional biogeography. Glob. Ecol. Biogeogr. 24, 1510–1521 (2015).Article 

    Google Scholar 
    Kuppler, J. et al. Global gradients in intraspecific variation in vegetative and floral traits are partially associated with climate and species richness. Glob. Ecol. Biogeogr. 29, 992–1007 (2020).Article 

    Google Scholar 
    Scheiter, S., Langan, L. & Higgins, S. I. Next-generation dynamic global vegetation models: learning from community ecology. New Phytol. 198, 957–969 (2013).Article 
    PubMed 

    Google Scholar 
    Taubert, F. et al. Confronting an individual-based simulation model with empirical community patterns of grasslands. PLoS ONE 15, e0236546 (2020).Roger, E. & Klistorner, S. (2016) Bioblitzes help science communicators engage local communities in environmental research. J. Sci. Commun. https://doi.org/10.22323/2.15030206 (2016).Legendre, P. & Legendre, L. Numerical Ecology 3rd edn (Elsevier, 2012).Warton, D. I. et al. Smatr 3—an R package for estimation and inference about allometric lines. Methods Ecol Evol 3, 257–259 (2012).Article 

    Google Scholar 
    Wolf, S. et al. iNaturalist_traits: iNaturalist trait maps version 1 (January 5, 2022) Zenodo https://doi.org/10.5281/zenodo.6671891 (2022). More

  • in

    Chemical forms of cadmium in soil and its distribution in French marigold sub-cells in response to chelator GLDA

    Sarwar, N. et al. Phytoremediation strategies for soils contaminated with heavy metals: Modifications and future perspectives. Chemosphere 171, 710–721 (2017).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Lin, H. M. et al. Cadmium-stress mitigation through gene expression of rice and silicon addition. Plant Growth Regul.: Int. J. Nat. Synthetic Regul. 81(1), 91–101 (2017).Article 
    CAS 

    Google Scholar 
    Pan, F. S. et al. Enhanced Cd extraction of oilseed rape (Brassica napus) by plant growth-promoting bacteria isolated from Cd hyperaccumulator Sedum alfredii Hance. Int. J. Phytorem. 19(1/6), 281–289 (2017).Article 
    CAS 

    Google Scholar 
    Puangprasert, S. & Prueksasit, T. Health risk assessment of airborne Cd, Cu, Ni and Pb for electronic waste dismantling workers in Buriram Province, Thailand. J. Environ. Manag. 252, 109601 (2019).Article 
    CAS 

    Google Scholar 
    Tipu, M. I. et al. Growth and physiology of maize (Zea mays L.) in a nickel-contaminated soil and phytoremediation efficiency using EDTA. J. Plant Growth Regul. 40(2), 774–786 (2021).Article 
    CAS 

    Google Scholar 
    Chaturvedi, N., Dhal, N. K. & Patra, H. K. EDTA and citric acid-mediated phytoextraction of heavy metals from iron ore tailings using Andrographis paniculata: A comparative study. Int. J. Min. Reclam. Environ. 29(1), 33–46 (2015).Article 
    CAS 

    Google Scholar 
    Wang, G. Y. et al. Heavy metal removal by GLDA washing: Optimization, redistribution, recycling, and changes in soil fertility. Sci. Total Environ. 569–570, 557–568 (2016).Article 
    ADS 
    PubMed 

    Google Scholar 
    Kołodyńska, D. Cu(II), Zn(II), Co(II) and Pb(II) removal in the presence of the complexing agent of a new generation. Desalination 267(2–3), 175–183 (2011).Article 

    Google Scholar 
    Guo, X. F. et al. Mixed chelators of EDTA, GLDA, and citric acid as washing agent effectively remove Cd, Zn, Pb, and Cu from soils. J. Soils Sediments 18(2), 835–844 (2017).
    Google Scholar 
    Wang, X. et al. Subcellular distribution and chemical forms of cadmiun in Bechmeria nivea L. Gaud. Environ. Exp. Bot. 62(3), 389–395 (2008).Article 
    CAS 

    Google Scholar 
    Gallego, S. M. et al. Unravelling cadmium toxicity and tolerance in plants: Insight into regulatory mechanisms. Environ. Exp. Bot. 83, 33–46 (2012).Article 
    CAS 

    Google Scholar 
    Clemens, S., Aarts, M. G. M., Thomine, S. & Verbruggen, N. Plant science: The key to preventing slow cadmium poisoning. Trends Plant Sci. 18(2), 92–99 (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Zhou, J. T. et al. Integration of cadmium accumulation, subcellular distribution, and physiological responses to understand cadmium tolerance in apple rootstocks. Front. Plant Sci. 8, 966 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yang, L. P., Zhu, J., Wang, P., Lyu, D. G. & Li, H. F. Effect of Cd on growth, physiological response, Cd subcellular distribution and chemical forms of Koelreuteria paniculata. Ecotoxicol. Environ. Saf. 160, 10–18 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Wang, W. J., Zhang, M. Z. & Liu, J. N. Subcellular distribution and chemical forms of Cd in Bougainvillea spectabilis Willd. as an ornamental phytostabilizer: An integrated consideration. Int. J. Phytorem. 20(11), 1087–1095 (2017).Article 

    Google Scholar 
    Weigel, H. J. & Jäger, H. J. Subcellular distribution and chemical form of cadmium in bean plants. Plant Physiol. 65(3), 480–482 (1980).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Khanna, K., Kohli, S. K., Ohri, P., Bhardwaj, R. & Ahmad, P. Agroecotoxicological aspect of Cd in soil–plant system: Uptake, translocation and amelioration strategies. Environ. Sci. Pollut. Res. 29, 30908–30934 (2022).Article 
    CAS 

    Google Scholar 
    Wei, Z. B., Chen, X. H., Wu, Q. T. & Tan, M. Biodegradable chelator GLDA induced remediation of heavy metal contaminated soil in Southeast Jingtian. Environ. Sci. 36(5), 1864–1869 (2015).CAS 

    Google Scholar 
    Wang, K., Liu, Y. H., Song, Z. G., Wang, D. & Qiu, W. W. Chelator complexes enhanced Amaranthus hypochondriacus L. phytoremediation efficiency in Cd-contaminated soils. Chemosphere 237, 124480 (2019).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Meng, N., Wang, M., Chen, L., Zheng, H. & Chen, S. B. Remediation effects of different herbaceous plants intercropping on Cd-contaminated soil. China Environ. Sci. 38(7), 2618–2624 (2018).CAS 

    Google Scholar 
    Jones, D. & Willett, V. Experimental evaluation of methods to quantify dissolved organic nitrogen (don) and dissolved organic carbon (doc) in soil. Soil Biol. Biochem. 38(5), 991–999 (2006).Article 
    CAS 

    Google Scholar 
    Su, F. L. et al. The distribution and enrichment characteristics of copper in soil and Phragmites australis of Liao River estuary wetland. Environ. Monit. Assess.: Int. J. 190(6), 1–9 (2018).Article 
    CAS 

    Google Scholar 
    Shahid, M., Dumat, C. & Khalid, S. Reviews of Environmental Contamination and Toxicology Vol. 241, 3–137 (Springer, 2016).
    Google Scholar 
    Yuliya, V. et al. Comparison of soil-to-root transfer and translocation coefficients of trace elements in vines of Chardonnay and Muscat white grown in the same vineyard. Sci. Hortic. 192, 89–96 (2015).Article 

    Google Scholar 
    Liu, Q. Q., Chen, Y. H., Shen, Z. G. & Zheng, L. Q. Roles of cell wall in plant heavy metal tolerance. Plant Physiol. J. 50(5), 605–611 (2014).
    Google Scholar 
    Zhen, S. et al. Foliar application of Zn reduces Cd accumulation in grains of late rice by regulating the antioxidant system, enhancing Cd chelation onto cell wall of leaves, and inhibiting Cd translocation in rice. Sci. Total Environ. 770, 145302 (2021).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Shi, Y. X. et al. Simulation of the absorption, migration and accumulation process of heavy metal elements in soil-crop system. Environ. Sci. 37(10), 3996–4003 (2016).
    Google Scholar 
    Yan, X. X. et al. Effect of foliar application of different manganese fertilizers on cadmium accumulation and subcellular distribution in pak choi. J. Agro Environ. Sci. 38(8), 1872–1881 (2019).
    Google Scholar 
    He, S., Wu, Q. & He, Z. Effect of DA-6 and EDTA alone or in combination on uptake, subcellular distribution and chemical form of Pb in Lolium perenne. Chemosphere 93(11), 2782–2788 (2013).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Li, C. C. et al. Integration of metal chemical forms and subcellular partitioning to understand metal toxicity in two lettuce (Lactuca sativa L.) cultivars. Plant Soil 384(1/2), 201–212 (2014).Article 
    CAS 

    Google Scholar 
    Li, D., He, T., Saleem, M. & He, G. Metalloprotein-specific or critical amino acid residues: Perspectives on plant-precise detoxification and recognition mechanisms under cadmium stress. Int. J. Mol. Sci. 23(3), 1734 (2022).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Perriguey, J., Sterckeman, T. & Morel, J. L. Effect of rhizosphere and plantrelated factors on the cadmium uptake by maize(Zea mays L.). Environ. Exp. Bot. 63(1/3), 333–341 (2008).Article 
    CAS 

    Google Scholar 
    Dai, S. et al. Effects of biochar amendments on speciation and bioavailability of heavy metals in coal-mine-contaminated soil. Hum. Ecol. Risk Assess. Int. J. 24(7), 1887–1900 (2018).Article 
    CAS 

    Google Scholar 
    Hou, S., Zheng, N., Tang, L., Ji, X. F. & Li, Y. Y. Effect of soil pH and organic matter content on heavy metals availability in maize (Zea mays L.) rhizospheric soil of non-ferrous metals smelting area. Environ. Monit. Assess. 191(10), 634 (2019).Article 
    PubMed 

    Google Scholar 
    Wu, H. J. et al. Effects of Astragalus smicuson cadmium effectiveness in paddy soil and cadmium accumulation in rice plant. Chin. Agric. Sci. Bull. 33(16), 105–111 (2017).ADS 

    Google Scholar 
    Jin, P. K., Liu, K. J. & Wang, X. B. Conversion and utilization of slowly biodegradable organic matter. Chin. J. Environ. Eng. 10(5), 2168–2174 (2016).CAS 

    Google Scholar 
    Kopáček, J. et al. Factors affecting the leaching of dissolved organic carbon after tree dieback in an unmanaged European mountain forest. Environ. Sci. Technol. 52(11), 6291–6299 (2018).Article 
    ADS 
    PubMed 

    Google Scholar 
    Anwar, S. et al. Impact of chelator-induced phytoextraction of cadmium on yield and ionic uptake of maize. Int. J. Phytorem. 19(6), 505–513 (2017).Article 
    CAS 

    Google Scholar 
    Wu, J. M., Xi, M. & Kong, F. L. Review of researches on the factors influencing the dynamics of dissolved organic carbon in soils. Geol. Rev. 59(5), 953–961 (2013).CAS 

    Google Scholar 
    AkzoNobel. Dissolvine GL® Technichal Brochure 1–5 (AkzoNobel Amsterdam, 2010).
    Google Scholar 
    Beygi, M. & Jalali, M. Assessment of trace elements (Cd, Cu, Ni, Zn) fractionation and bioavailability in vineyard soils from the Hamedan, Iran. Geoderma 337, 1009–1020 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Gul, I. et al. Comparative effectiveness of organic and inorganic amendments on cadmium bioavailability and uptake by Pelargonium hortorum. J. Soils Sediments 19(5), 2346–2356 (2019).Article 
    CAS 

    Google Scholar 
    Wang, H., Sun, L. N., Li, H. B. & Sun, T. Y. Effect of different chelators application on Cd accumulation in metal polluted soils by Beta vulgaris var. cicla L. Ecol. Environ. 17(6), 2249–2252 (2008).
    Google Scholar 
    Zhang, G. X. et al. Effects of biochars on the availability of heavy metals to ryegrass in an alkaline contaminated soil. Environ. Pollut. 218, 513–522 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Gu, M. H. et al. Effects of manganese application on the formation of manganese oxides and cadmium fixation in soil. Ecol. Environ. Sci. 229(2), 360–368 (2020).
    Google Scholar 
    Bradl, H. B. Adsorption of heavy metal ions on soils and soils constituents. J. Colloid Interface Sci. 277(1), 1–18 (2004).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar  More