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SoilContents of risk metals in soilsLands of localities from which soil and plant samples were taken belong to agricultural lands.Soil reaction is one of the factors that most affects the behaviour of heavy metals in soil. Low pH values pose a risk of reduced nutrient intake and increase the availability of heavy metals for plants29,30.The presence of risk elements in the soil was evaluated based on their contents in bioavailable form (mobile forms), determined in soil extracts NH4NO3, and the total contents of risk elements were determined in soil extract by aqua regia (Table 1).Table 1 The contents of risk elements (Cu, Ni, Pb, Cd, Hg, Mn) in soil (mg/kg).Full size tableAccessible heavy metals for plants are those which are present in the soil solution as soluble components or those which are easily dissolved by root exudates31. The highest Cu contents determined in soil extract by NH4NO3, were in the cadastre of Gemerská Poloma (max. 0.390 mg/kg) (Table 1). However, even the highest determined concentration of Cu in its bioavailable form did not exceeded the determined critical value for this element18. Nickel is a beneficial element for plants. Elevated Ni concentrations in soils have a potential negative effect on plants32. Content of bioavailable forms of nickel is lower than the determined critical value in all analysed samples. Cadmium and lead present a risk to agricultural activity in this area. Cadmium in soil is highly bioavailable and has higher mobility in plants compared to other heavy metals. It is easily transported by roots to shoots. In contrast, lead is one of the least mobile heavy metals. It is naturally concentrated in the upper layers of the soil33. The contents of the available forms of cadmium and lead exceed the critical values for these elements. In case of lead, the determined contents are from 0.257 Henckovce to 0.676 Gemerská Poloma. Takáč et al.34 determined in 20 soil samples from the Central Spiš region 7.2–257.6 mg Cu/kg soil and 1.0–84.8 mg Pb/kg in their potentially mobilizable form and 0.4–1.4 mg Cu/kg soil and 4.3–7.1 mg Pb/kg in their mobile form. In comparison with our results, Vilček et al.35 determined a lower content of Cd (0.04), Pb (0.17), Ni (0.15) and higher Cu content (0.48) mg/kg in forms accessible to plants in 16 soil samples from locality Nižná Slaná in the years 2006–2008. However, high concentrations of metals in soil do not necessarily mean the availability of metals for plants36. As a result, extractable Mn is often a better indicator of Mn availability. Mn2+ is generally considered to be bioavailable22. The highest concentration of Mn was measured in soil samples from the cadastre of Nižná Slaná. On the contrary, the lowest concentrations were detected in samples from Gemerská Poloma cadastre, which is the furthest cadastre from the source. No critical limit is set up for manganese according to Slovak legislation, it is not possible to classify these soils as contaminated/uncontaminated. For comparison, the EDTA-extractable content of Mn ranged from 22.7 to 127 mg/kg dry soil (China)29; the mobile concentrations between 0.32 and 202.0 mg/kg and the available concentrations from 5.4 to 126.3 mg/kg (Egypt)37.Based on results of statistical analysis, significant higher content of Cu, Pb and Cd can be stated in samples from Gemerská Poloma cadastre. These soils are classified as gley fluvisols, soils from the other two localities are cambisols (from medium heavy to light) and acid cambisols (Henckovce), cambisols from medium heavy to light and typically acid cambisols (Nižná Slaná). The soil profile of fluvisols is repeatedly disrupted by floods, which often enriches them with a new layer of sludge sediments2.Another method for determination of metal content in soil is mineralisation using aqua regia, which dissolves most of the soil constituents except those strongly bound in silicate minerals. This content is sometimes referred to as pseudototal (determined in aqua regia). In this way, all elements that are likely to become bioavailable in the long term are determined38.Pseudototal contents of risk metals (Table 1) determined in soil extract using aqua regia were higher than their limit value in case of Cu (Gemerská Poloma cadastre), Cd (all cadastres) and Hg (cadastre of Henckovce and Gemerská Poloma).Due to the fact that the hygienic condition of agricultural soils is assessed according to the exceeding of the limit values of at least one risk substance, the monitored plots can be classified as contaminated (Cu  > 60.0, Cd  > 0.7, Hg  > 0.5 mg/kg soil).Manganese is not classified as risk element in Slovak legislation.Tóth et al.39 classified European soils into four categories: (1) no detectable content of HM, (2) the concentration of the investigated element is above the threshold value (Hg 0.5, Cd 1, Cu 100, Pb 60 and Ni 50 mg/kg), but below the lower guideline value (Hg 2, Cd 10, Cu 150, Pb 200 and Ni 100 mg/kg), (3) concentration of one or more element exceeds the lower guideline value but is below the higher guideline value (Hg 5, Cd 20, Cu 200, Pb 750 and Ni 150 mg/kg), (4) samples having concentrations above the higher guideline value.In comparison with the threshold and guideline values, soils in cadastres of Gemerská Poloma (Cu), Henckovce, Nižná Slaná, Gemerská Poloma (Cd, Hg) represent the ecological risk. Threshold and guideline values for Mn were not defined.The Spiš region and the northern part of the Gemer region belong to the most polluted areas in Slovakia in terms of soil contamination due to mining and metallurgical activities that have been carried out here in the past. Soils near the sludge in Nižná Slaná contain 3.17–53.3 (14.2–301, 0.71–20.6, 3.33–177, 12.9–223 and 675–11,510, respectively) mg Cd (Cu, Hg, Ni, Pb and Mn, respectively)/kg of soil14. In loaded area of Dongchuan, (China), contained Cd (Cu, Hg, Ni and Pb, resp.) 0.20–3.57 (45.38–2026, 0.02–0.23, 24.06–95.9 and 6.83–146.6, resp.) mg/kg40. In contrast, in the agricultural area of Punjab of the India, the soil contamination was caused by an excessive use of agrochemicals and polluted irrigation sources. Increased Cu (Pb and Cd) contents were determined in the soil samples: 9.0–48.5 (5.5–9.67 and 0.516–1.58, resp.) mg/kg41.However, in most cases, a large portion of the total element content is not available for immediate uptake by plants. Available forms represent a small proportion of this total content which is potentially available to plants. Availability is affected by many factors, including pH, redox state, macronutrient levels, available water content and temperature29,33,36,38.Indicators of soil contaminationContamination factors and degree of contaminationThe contamination character may be described in a uniform, adequate and standardised way by means of the contamination factor and the degree of contamination. Hakanson24 reported four Contamination degrees of individual metal (({mathrm{C}}_{mathrm{f}}^{mathrm{i}})) – low (({mathrm{C}}_{mathrm{f}}^{mathrm{i}}) < 1), moderate (1 ≤ ({mathrm{C}}_{mathrm{f}}^{mathrm{i}})   More

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ParticipantsData were drawn from the NCDS, which is a nationally representative study that has followed a cohort of participants all born in a single week in the United Kingdom since 1958. Since birth, they have been followed up a total of 11 times at ages 7, 11, 16, 23, 33, 42, 44, 46, 50 and 55. As data on time spent caring for grandchildren is only available from the most recent interview, all analyses here are cross-sectional, with all women included in the sample being aged either 55 or 56 (depending on whether the interview was conducted in 2013 or 2014) and representing the third generation of women in Fig. 1. The sample was limited to women who had at least one parent alive and at least one grandchild (n = 934). Data from the NCDS are available from the UK Data Service, and the participant characteristics shown in Supplementary Table S1.VariablesHours spent helping parents per weekInformation regarding parental caregiving was included as a count variable. In the most recent interviews, participants were asked whether they ever do various activities for their parents (e.g. shopping for them, helping with basic personal needs, giving them lifts, etc.), and if they do, how many hours on average per week do they spend doing so. Any women who reported not helping their parents do any of the activities were coded as helping their parents for zero hours per week.Hours spent caring for grandchildren per monthThe number of hours spent caring for grandchildren per month was also included as a count variable. Women were asked whether they ever look after their grandchildren without the grandchild’s parents being present, and if they do, at what frequency and for how many hours. Women who stated that they did not care for their grandchildren or did so less often than monthly were coded as caring for their grandchildren for zero hours per month. This measure also includes overnight stays.Fecundity status at age 55Fecundity status was derived from information on age, year and reason for last menstrual period, which was collected at ages 44, 50, and 55. Based on this, a binary categorical variable was derived where women were coded as either ‘Still menstruating’ or ‘No longer menstruating’. The latter category comprised of women who were post-menopausal or who had stopped menstruating for another reason, such as a surgical menopause. Women who had stopped menstruating due to menopause or other reasons were grouped together as the direct fitness implications of no longer menstruating are the same, regardless of the reason for it.Control variablesCovariates included were selected based on their expected effect on the woman’s ability to help other family members. As a proxy of socioeconomic status, the age at which the woman left education was included. Employment status was utilised to give an indication of the woman’s time constraints (i.e. if she was employed, it can be expected she had less time to care for kin)24, with women being coded as either employed, unemployed, or other, with the latter category including those who are doing something other than formal employment but do not classify themselves as unemployed (e.g. retired, volunteering, studying, etc.). Self-perceived health was used as a measure of how physically able the woman is to help family members25, and number of grandchildren was also included to adjust for how many grandparenting responsibilities a woman had. We also included information on the mortality status of the woman’s parents (i.e. whether she had both parents alive or not), which was derived from interviews at ages 7, 11, 16, 23, 42, 46, 50 and 55. The focal woman’s mother’s and father’s age at birth (collected in the perinatal interview) were also included to control for the amount of help her parents may need, as older parents would expected to be more in need of assistance. Finally, in models predicting hours spent caring for parents, time spent caring for grandchildren was adjusted for, and vice versa for models where hours spent caring for grandchildren was the outcome.AnalysesTime spent helping parents and caring for grandchildren were both modelled using zero-inflated negative binomial regression (ZINB). This modelling procedure was selected both due to the over-dispersed nature of the data with excess zeros, and because zero-inflated models allow for zeros to be generated through two distinct processes. Here, the model distinguishes between excess zeroes, which occur when the event could not have happened, and true zeros, which occur when there could have been an event. Therefore, the model estimates a binary outcome (does not care versus does care) and a count outcome (the number of hours spent caring). This method is theoretically appropriate, as there are many different reasons people would offer no care to kin: while some people may choose to invest less, for some people the choice is out of their control, with external factors influencing caring behaviours, such as living far away from kin26. In addition to this, ZINB was found to better fit the data than negative binomial regression (Supplementary Table S2).Time spent helping parents was first modelled. A ‘base’ model was first made containing the age the woman left education, employment status, marital status, self-perceived health, number of grandchildren, parent mortality status, age of parents, and time spent caring for grandchildren. Fecundity status was subsequently added, and model fitting then carried out on these two models, utilising their Akaike Information Criterion (AIC) value to understand whether a model including fecundity better fit the data than one without. The model with the lowest AIC value is taken to best fit the data. As AIC values penalise models for complexity, it means the model with the most terms will not automatically be selected as the best. The ΔAIC was also calculated, which is the difference between the candidate models AIC and the AIC value of the best fitting candidate model. If the ΔAIC value is ≤ 2, then it indicates that there is still good evidence to support the candidate model, meaning that a candidate model with a ΔAIC of ≤ 2 is almost as good as the best fitting model. A ΔAIC value of between 4 and 7 is taken to indicate the candidate model has considerably less support, and a ΔAIC of greater than 10 indicates there is no support for the candidate model27. The Akaike weights (wi) were also calculated to evaluate model fit, which give the probability that the candidate model is the best among the set of presented candidate models27. The same procedure was then used to model time spent caring for grandchild per month: a model including just the covariates was first made, but this time adjusting for time spent helping parents rather than time caring for grandchildren, with fecundity status then being added, and model fitting was once again carried out using the methods outlined above. All analyses were carried in R using the zeroinfl function with a negative binomial distribution specified28, and model fitting carried out with the package AICcmodavg29. All visualisations were created using ggplot230. More

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Centro de Investigação em Biodiversidade e Recursos Genéticos/Research Network in Biodiversity and Evolutionary Biology, Campus Agrário de Vairão, Universidade do Porto, Vairão, PortugalNuno Queiroz, Ana Couto, Marisa Vedor, Ivo da Costa, Gonzalo Mucientes & António M. SantosMarine Biological Association of the United Kingdom, Plymouth, UKNuno Queiroz, Nicolas E. Humphries, Lara L. Sousa, Samantha J. Simpson, Emily J. Southall & David W. SimsDepartamento de Biologia, Faculdade de Ciências da Universidade do Porto, Porto, PortugalMarisa Vedor & António M. SantosUWA Oceans Institute, Indian Ocean Marine Research Centre, University of Western Australia, Crawley, Western Australia, AustraliaAna M. M. SequeiraSchool of Biological Sciences, University of Western Australia, Crawley, Western Australia, AustraliaAna M. M. SequeiraSpanish Institute of Oceanography, Santa Cruz de Tenerife, SpainFrancisco J. AbascalAbercrombie and Fish, Port Jefferson Station, NY, USADebra L. AbercrombieMarine Biology and Aquaculture Unit, College of Science and Engineering, James Cook University, Cairns, Queensland, AustraliaKatya Abrantes, Adam Barnett, Richard Fitzpatrick & Marcus SheavesInstitute of Natural and Mathematical Sciences, Massey University, Palmerston North, New ZealandDavid Acuña-MarreroUniversidade Federal Rural de Pernambuco (UFRPE), Departamento de Pesca e Aquicultura, Recife, BrazilAndré S. Afonso, Natalia P. A. Bezerra, Fábio H. V. Hazin, Fernanda O. Lana, Bruno C. L. Macena & Paulo TravassosMARE, Marine and Environmental Sciences Centre, Instituto Politécnico de Leiria, Peniche, PortugalAndré S. AfonsoMARE, Laboratório Marítimo da Guia, Faculdade de Ciências da Universidade de Lisboa, Cascais, PortugalPedro Afonso, Jorge Fontes & Frederic VandeperreInstitute of Marine Research (IMAR), Departamento de Oceanografia e Pescas, Universidade dos Açores, Horta, PortugalPedro Afonso, Jorge Fontes, Bruno C. L. Macena & Frederic VandeperreOkeanos – Departamento de Oceanografia e Pescas, Universidade dos Açores, Horta, PortugalPedro Afonso, Jorge Fontes & Frederic VandeperreDepartment of Environmental Affairs, Oceans and Coasts Research, Cape Town, South AfricaDarrell Anders, Michael A. Meÿer, Sarika Singh & Laurenne B. SnydersLarge Marine Vertebrates Research Institute Philippines, Jagna, PhilippinesGonzalo AraujoFins Attached Marine Research and Conservation, Colorado Springs, CO, USARandall ArauzPrograma Restauración de Tortugas Marinas PRETOMA, San José, Costa RicaRandall ArauzMigraMar, Olema, CA, USARandall Arauz, Sandra Bessudo Lion, Eduardo Espinoza, Alex R. Hearn, Mauricio Hoyos, James T. Ketchum, A. Peter Klimley, Cesar Peñaherrera-Palma, George Shillinger & German SolerInstitut de Recherche pour le Développement, UMR MARBEC (IRD, Ifremer, Univ. Montpellier, CNRS), Sète, FrancePascal Bach, Antonin V. Blaison, Laurent Dagorn, John D. Filmalter, Fabien Forget, Francois Poisson, Marc Soria & Mariana T. TolottiBiology Department, University of Massachusetts Dartmouth, Dartmouth, MA, USADiego Bernal & Heather MarshallRed Sea Research Center, Division of Biological and Environmental Science and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi ArabiaMichael L. Berumen, Jesse E. M. Cochran & Carlos M. DuarteFundación Malpelo y Otros Ecosistemas Marinos, Bogota, ColombiaSandra Bessudo Lion, Felipe Ladino, Lina Maria Quintero & German SolerHopkins Marine Station of Stanford University, Pacific Grove, CA, USABarbara A. Block, Taylor K. Chapple, George Shillinger & Timothy D. WhiteDepartment of Biological Sciences, Florida International University, North Miami, FL, USAMark E. Bond, Demian D. Chapman & Yannis P. PapastamatiouInstituto de Ciências do Mar, Universidade Federal do Ceará, Fortaleza, BrazilRamon BonfilCSIRO Oceans and Atmosphere, Hobart, Tasmania, AustraliaRussell W. Bradford & Barry D. BruceSchool of Aquatic and Fishery Sciences, University of Washington, Seattle, WA, USACamrin D. BraunBiology Department, Woods Hole Oceanographic Institution, Woods Hole, MA, USACamrin D. Braun & Simon R. ThorroldShark Research and Conservation Program, Cape Eleuthera Institute, Eleuthera, BahamasEdward J. Brooks, Annabelle Brooks & Sean WilliamsUniversity of Exeter, Exeter, UKAnnabelle BrooksSouth Atlantic Environmental Research Institute, Stanley, Falkland IslandsJudith BrownDepartment of Biological Sciences, The Guy Harvey Research Institute, Nova Southeastern University, Dania Beach, FL, USAMichael E. Byrne, Mahmood Shivji, Jeremy J. Vaudo & Bradley M. WetherbeeSchool of Natural Resources, University of Missouri, Columbia, MO, USAMichael E. ByrneLife and Environmental Sciences, University of Iceland, Reykjavik, IcelandSteven E. CampanaSchool of Marine Science and Policy, University of Delaware, Lewes, DE, USAAaron B. Carlisle & Gregory B. SkomalMassachusetts Division of Marine Fisheries, New Bedford, MA, USAJohn ChisholmMarine Research Facility, Jeddah, Saudi ArabiaChristopher R. Clarke & James S. E. LeaPSL, Labex CORAIL, CRIOBE USR3278 EPHE-CNRS-UPVD, Papetoai, French PolynesiaEric G. CluaAgence de Recherche pour la Biodiversité à la Réunion (ARBRE), Réunion, Marseille, FranceEstelle C. CrocheletInstitut de Recherche pour le Développement, UMR 228 ESPACE-DEV, Réunion, Marseille, FranceEstelle C. CrocheletSave Our Seas Foundation–D’Arros Research Centre (SOSF-DRC), Geneva, SwitzerlandRyan Daly & Clare A. Keating DalySouth African Institute for Aquatic Biodiversity (SAIAB), Grahamstown, South AfricaRyan Daly, John D. Filmalter, Enrico Gennari & Alison A. KockDepartment of Fisheries Evaluation, Fisheries Research Division, Instituto de Fomento Pesquero (IFOP), Valparaíso, ChileDaniel Devia CortésSchool of Biological, Earth and Environmental Sciences, University College Cork, Cork, IrelandThomas K. Doyle & Luke HarmanMaREI Centre, Environmental Research Institute, University College Cork, Cork, IrelandThomas K. DoyleCollege of Science and Engineering, Flinders University, Adelaide, South Australia, AustraliaMichael Drew, Matthew Heard & Charlie HuveneersDepartment of Conservation, Auckland, New ZealandClinton A. J. DuffySouth African Institute for Aquatic Biodiversity, Geological Sciences, UKZN, Durban, South AfricaThor EriksonDireccion Parque Nacional Galapagos, Puerto Ayora, Galapagos, EcuadorEduardo EspinozaAustralian Institute of Marine Science, Indian Ocean Marine Research Centre (UWA), Crawley, Western Australia, AustraliaLuciana C. Ferreira, Mark G. Meekan & Michele ThumsDepartment of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA, USAFrancesco FerrettiOCEARCH, Park City, UT, USAG. Chris FischerBedford Institute of Oceanography, Dartmouth, Nova Scotia, CanadaMark Fowler, Warren Joyce & Anna MacDonnellNational Institute of Water and Atmospheric Research, Wellington, New ZealandMalcolm P. Francis & Warrick S. LyonBeneath the Waves, Herndon, VA, USAAustin J. GallagherRosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USAAustin J. Gallagher, Neil Hammerschlag & Emily R. NelsonOceans Research Institute, Mossel Bay, South AfricaEnrico GennariDepartment of Ichthyology and Fisheries Science, Rhodes University, Grahamstown, South AfricaEnrico Gennari & Alison TownerSARDI Aquatic Sciences, Adelaide, South Australia, AustraliaSimon D. Goldsworthy & Paul J. RogersZoological Society of London, London, UKMatthew J. Gollock & Fiona LlewellynGalapagos Whale Shark Project, Puerto Ayora, Galapagos, EcuadorJonathan R. GreenGriffith Centre for Coastal Management, Griffith University School of Engineering, Griffith University, Gold Coast, Queensland, AustraliaJohan A. GustafsonSaving the Blue, Cooper City, FL, USATristan L. GuttridgeSmithsonian Tropical Research Institute, Panama City, PanamaHector M. GuzmanLeonard and Jayne Abess Center for Ecosystem Science and Policy, University of Miami, Coral Gables, FL, USANeil HammerschlagGalapagos Science Center, San Cristobal, Galapagos, EcuadorAlex R. HearnUniversidad San Francisco de Quito, Quito, EcuadorAlex R. HearnBlue Water Marine Research, Tutukaka, New ZealandJohn C. HoldsworthUniversity of Queensland, Brisbane, Queensland, AustraliaBonnie J. HolmesMicrowave Telemetry, Columbia, MD, USALucy A. Howey & Lance K. B. JordanPelagios-Kakunja, La Paz, MexicoMauricio Hoyos & James T. KetchumMote Marine Laboratory, Center for Shark Research, Sarasota, FL, USARobert E. Hueter, John J. Morris & John P. TyminskiBiological Sciences, University of Windsor, Windsor, Ontario, CanadaNigel E. HusseyCape Research and Diver Development, Simon’s Town, South AfricaDylan T. IrionInstitute of Zoology, Zoological Society of London, London, UKDavid M. P. JacobyCentre for Sustainable Aquatic Ecosystems, Harry Butler Institute, Murdoch University, Perth, Western Australia, AustraliaOliver J. D. JewellDyer Island Conservation Trust, Western Cape, South AfricaOliver J. D. Jewell & Alison TownerBlue Wilderness Research Unit, Scottburgh, South AfricaRyan JohnsonUniversity of California Davis, Davis, CA, USAA. Peter KlimleyCape Research Centre, South African National Parks, Steenberg, South AfricaAlison A. KockShark Spotters, Fish Hoek, South AfricaAlison A. KockInstitute for Communities and Wildlife in Africa, Department of Biological Sciences, University of Cape Town, Rondebosch, South AfricaAlison A. KockWestern Cape Department of Agriculture, Veterinary Services, Elsenburg, South AfricaPieter KoenDepartamento de Biologia Marinha, Universidade Federal Fluminense (UFF), Niterói, BrazilFernanda O. LanaDepartment of Zoology, University of Cambridge, Cambridge, UKJames S. E. LeaAtlantic White Shark Conservancy, Chatham, MA, USAHeather MarshallFisheries and Aquaculture Centre, Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, AustraliaJaime D. McAllister, Jayson M. Semmens, German Soler & Kilian M. StehfestPontificia Universidad Católica del Ecuador Sede Manabi, Portoviejo, EcuadorCesar Peñaherrera-PalmaMarine Megafauna Foundation, Truckee, CA, USASimon J. Pierce & Christoph A. RohnerConservation and Fisheries Department, Ascension Island Government, Georgetown, Ascension Island, UKAndrew J. RichardsonMarine Conservation Society Seychelles, Victoria, SeychellesDavid R. L. RowatCORDIO, East Africa, Mombasa, KenyaMelita SamoilysUpwell, Monterey, CA, USAGeorge ShillingerDepartment of Zoology and Institute for Coastal and Marine Research, Nelson Mandela University, Port Elizabeth, South AfricaMalcolm J. SmaleNational Institute of Polar Research, Tachikawa, Tokyo, JapanYuuki Y. WatanabeSOKENDAI (The Graduate University for Advanced Studies), Tachikawa, Tokyo, JapanYuuki Y. WatanabeCentre for Ecology and Conservation, University of Exeter, Penryn, UKSam B. WeberDepartment of Biological Sciences, University of Rhode Island, Kingston, RI, USABradley M. WetherbeeDepartment of Oceanography and Environment, Fisheries Research Division, Instituto de Fomento Pesquero (IFOP), Valparaíso, ChilePatricia M. ZárateDepartment of Biological Sciences, Macquarie University, Sydney, New South Wales, AustraliaRobert HarcourtSchool of Life and Environmental Sciences, Deakin University, Geelong, Victoria, AustraliaGraeme C. HaysAZTI – BRTA, Pasaia, SpainXabier IrigoienIKERBASQUE, Basque Foundation for Science, Bilbao, SpainXabier IrigoienInstituto de Fisica Interdisciplinar y Sistemas Complejos, Consejo Superior de Investigaciones Cientificas, University of the Balearic Islands, Palma de Mallorca, SpainVictor M. EguiluzWildlife Conservation Research Unit, Department of Zoology, University of Oxford, Tubney, UKLara L. SousaOcean and Earth Science, National Oceanography Centre Southampton, University of Southampton, Southampton, UKSamantha J. Simpson & David W. SimsCentre for Biological Sciences, University of Southampton, Southampton, UKDavid W. SimsN.Q. and D.W.S. planned the data analysis. N.Q. led the data analysis with contributions from M.V., A.M.M.S. and D.W.S. N.E.H. contributed analysis tools. A.M.M.S. undertook linear-regression modelling. D.W.S. led the manuscript writing with contributions from N.Q., N.E.H., A.M.M.S and all authors. Six of the original authors were not included in the Reply authorship; two authors retired from science and the remaining four, although supportive of our Reply, declined to join the authorship due to potential conflicts of interest with the authors of the Comment and/or their institutions. More

Study areaThe Pearl River Delta (112°45′–113°50′ E, 21°31′–23°10′ N) is located in the central and southern parts of Guangdong Province, including the lower reaches of the Pearl River, adjacent to Hong Kong and Macao, and facing Southeast Asia across the sea with convenient land and sea transportation. As shown in Fig. 1, the Pearl River Delta region includes nine prefecture-level cities, namely Guangzhou, Shenzhen, Zhongshan, Zhuhai, Dongguan, Zhaoqing, Foshan, Huizhou, and Jiangmen.Figure 1Geographical location of Pearl River Delta drawn in ArcGIS 10.6.Full size imageData sourceThe research framework of this paper is shown in Fig. 2, and the data sources are as follows. Taking the basin as the research unit, the raster data of 30 m and 1 km were analyzed by zoning statistics:

(1)

China’s land-use raster data for 1990, 2000, 2010, and 2018 were obtained from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (http://www.resdc.cn), with a spatial resolution of 30 m. According to land resources and their utilization attributes, the dataset divides land cover types into six first-level categories: cultivated land, woodland, grassland, water area, construction land, unused land, and land reclamation from ocean. The land urbanization rate (LUR) refers to the proportion of construction land in the whole region, which is calculated by dividing the area of construction land by the area of all land use types.

(2)

Raster data of population density (POP) from 1990, 2000, 2010, and 2015 were obtained from the Environment and Resources Data Cloud Platform of the Chinese Academy of Sciences, with a spatial resolution of 1 km. Owing to the stable growth of population density under normal circumstances, the population density data of 2018 were obtained by linear fitting based on POP data from 2010 and 2015.

(3)

Nighttime Light (NTL) raster data from 1992 to 2018 were obtained from the Nature journal data (https://doi.org/10.6084/m9.figshare.9828827.v2) with a spatial resolution of 500 m45 Calibration was performed to eliminate the differences in the DMSP (1992–2013) and VIIRS (2012–2018) data, generating a complete and consistent NTL dataset on a global scale.

Figure 2Research framework.Full size imageLand-use information TUPUThe land-use information graph is a geospatial analysis model combining attributes, processes, and spaces, which can reflect the spatial differences and temporal changes in land-use types46. In its function expression, let the state variables be (pleft( {p_{1} ,p_{2} ,p_{3} , ldots ,p_{n} } right)), and then set p as a function of spatial position r and time t, as follows:$$ begin{array}{*{20}c} {p = fleft( {r,t} right)} \ end{array} $$
(1)
where (p) represents land-use characteristics. (1) To realize the spatial description of land attributes, when t is constant, the function relation of (p) changing with (r) is constructed. (2) The process description of land attributes can be realized, and when (r) is constant, the function relation of (p) changing with (t) can be constructed. The combination of these two functions can form a conceptual model of the land-use information graph and realize a composite study of land space, process, and attributes.Habitat qualityHabitat quality evaluationWe used InVEST-HQ to evaluate the habitat quality in the Pearl River Delta region. Based on land-use types, InVEST-HQ calculated the habitat degradation degree and habitat quality index by using threat factors, the sensitivity of different habitat types to threat factors, and habitat suitability15. The InVEST-HQ model was co-developed by Stanford University, the Nature Conservancy, and the World Wide Fund for Nature15. InVEST-HQ has a low demand for data and a better spatial visualization effect, which is widely used in the field of urban ecology47,48,49. For example, The InVEST-HQ model has been used to assess dynamic changes in habitat quality in Scottish11, China50,51 and Portugal47. Habitat degradation and habitat quality were calculated using the following formulas:$$ begin{array}{*{20}c} {Q_{{xj}} = ~H_{j} left[ {1 – left( {frac{{D_{{xj}}^{2} }}{{D_{{xj}}^{2} + k^{2} )}}} right)} right]} \ end{array} $$
(2)
$$ begin{array}{*{20}c} {D_{{xj}} = ~mathop sum limits_{{r = 1}}^{r} mathop sum limits_{{y = 1}}^{y} left( {frac{{w_{r} }}{{mathop sum nolimits_{{r = 1}}^{r} w_{r} }}} right)r_{y} i_{{rxy}} beta _{x} S_{{jr}} } \ end{array} $$
(3)
where (Q_{{xj}}) is the habitat quality of grid x in land-use type j, (H_{j}) is the habitat suitability of land-use type j, (D_{{xj}}) is the habitat degradation degree of grid x in land-use type j, k is the half-satiety sum constant, r is the number of threat factors, and y is the relative sensitivity of threat sources. (r_{y} ,w_{r}), and (i_{{rxy}}) are, respectively, the interference intensity and weight of the grid where the threat factor r is located, and the interference generated by the habitat. (beta _{x} ,S_{{jr}}) are the anti-disturbance ability of habitat type x and its relative sensitivity to various threat sources, respectively.The value range of habitat degradation degree is [0, 1], and the larger the value, the more serious the habitat degradation. The value of habitat quality is between 0 and 1, and the higher the value, the better the habitat quality.$$ begin{array}{*{20}c} {Linear,attenuation:~i_{{rxy}} = 1 – left( {d_{{xy}} /d_{{r,max}} } right)} \ end{array} $$
(4)
$$ begin{array}{*{20}c} {Exponential,decay:~i_{{rxy}} = expleft[ { – 2.99d_{{xy}} /d_{{r{text{~}}max}} } right]} \ end{array} $$
(5)

where (d_{{xy}}) is the straight-line distance between grids x and y, and (d_{{r,max}}) is the maximum threat distance of threat factor r.Five categories of documentation are prepared before using InVEST-HQ: LULC maps, threat factor data, threat sources, accessibility of degradation sources, habitat types and their sensitivity to each threat. Threat sources were divided into Cropland, City/town, Rural settlements, Other construction land, Unused land, and land applications. The maps of threat sources are generated in ArcGIS. For example, in the map of threat sources of cultivated land, the raster value of cultivated land is set to 1, and the raster value of other land types is set to 0. Distance between habitats and threat sources, weight of threat factors, decay type of threats factors, habitat suitability and the sensitivity of different habitat types to threat factors were derived from previous studies in similar regions2,25,38,39,50 and user guide manual of InVEST model15, as shown in Tables 1 and 2.Table 1 Threat factors and related coefficients.Full size tableTable 2  Sensitivity of habitat types to each threat factor.Full size tableHabitat quality change index and contribution indexThe CI was used to analyze the causes of the changes in habitat quality, and the following formula was used to qu2,25,38,39,50antitatively represent the contribution of land-use conversion to habitat quality change. In this study, the total value of habitat quality loss caused by land transfer in areas related to construction land expansion from 1990 to 2018 can be expressed as follows:$$ begin{array}{*{20}c} {CI~ = ~frac{{mathop sum nolimits_{1}^{n} left( {Q_{{ij2018}} – Q_{{xj1990}} } right)}}{n}} \ end{array} $$
(6)

where n is the grid number of cultivated land transferred to construction land.To analyze the relationship between land-use change and habitat quality, the HQCI was constructed to describe the mean value of habitat quality reduction caused by land transfer in the areas related to construction land expansion during the study period. The formula is as follows:$$ begin{array}{*{20}c} {HQCI~ = CI_{{ij}} /S_{{ij}} } \ end{array} $$
(7)
where (CI_{{ij}}) represents the total value of habitat quality change when land-use type (i) is converted into land-use type (j), and (S_{{ij}}) represents the area converted from land-use type (i) into land-use type (j). The positive and negative values of HQCI, respectively, represent the positive and negative impacts of land-use change on the habitat, and the higher the absolute value of HQCI, the greater the impact.Correlation analysisGeographically weighted regressionBased on traditional OLS, GWR establishes local spatial regression and considers spatial location factors, which can effectively analyze the spatial heterogeneity of various elements at different locations52. The calculation formula is as follows:$$ Y_{i} = ~beta _{0} left( {mu _{i} ,v_{i} } right) + sum kbeta _{k} left( {mu _{i} ,v_{i} } right)X_{{ik}} + varepsilon _{i} $$where (Y_{i}) is the coupling coordination degree of the ith sample point, (left( {mu _{i} ,v_{i} } right)) is the spatial position coordinate of the ith sample point, (beta _{k} left( {mu _{i} ,v_{i} } right)) is the value of the continuous function (beta _{k} left( {mu ,v} right)) at (left( {mu _{i} ,v_{i} } right)), (X_{{ik}}) is the independent variable, (varepsilon _{i}) is the random error term, and k is the number of spatial units.To simplify the complicated urbanization process, it was divided into three aspects: economic urbanization, population urbanization, and land urbanization according to the existing research38. The NTL, POP, and LUR were used to represent the economic development, population scale, and land urbanization level of the city.The research unit is a river basin, which has both natural and social attributes. It is a relatively independent and complete system, which can connect and explain the coupling phenomenon of society, economy, and nature53. The hydrological analysis module in ArcGIS was used to divide the research area into 374 small basins. When calculating the cumulative flow of the grid, 100,000 was used as the threshold value, and basins less than 5 km2 were combined with the adjacent basins.Zone classification using the Self-organizing feature mapping neural networkThe SOFM neural network was proposed by Kohonen, a Finnish scholar, and constructed by simulating a “lateral inhibition” phenomenon in the human cerebral cortex. It has been widely applied in classification research in geographic and land system science42,43. The advantages of the SOFM neural network in classifying the coupling relationship between urbanization and habitat quality are as follows : (1) it simulates human brain neurons through unsupervised learning, which is objective and reliable. (2) It maintains the data topology during self-learning, training, and simulation to obtain reasonable partition results and identify the differences between different basins. (3) For massive data, the SOFM network has a good clustering function while maintaining its characteristics and uses the weight vector of the output node to represent the original input. The SOFM neural network can compress the data while maintaining a high similarity between the compression results and the original input data54. We exported the data from ArcGIS, and conducted cluster analysis on the four factors of NTL, POP, LUR and habitat quality using SOFM. Finally, the analysis results are imported into ArcGIS for display. More

An umbrella-shaped structure of unknown function crowns a recently described species of fairy lantern. Credit: Siti Munirah Mat Yunoh et al./PhytoKeys (CC BY 4.0)

Conservation biology
07 July 2021
Newfound ‘fairy lantern’ could soon be snuffed out forever

Wild boars have destroyed three of the four known specimens of a bizarre plant in the forests of Malaysia.

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Researchers have discovered a new species of ‘fairy lantern’, leafless plants that look like tiny glowing lights. Sadly, however, the organism might already be on the verge of extinction.Plants in the genus Thismia, colloquially called ‘fairy lanterns’, draw nutrients from underground fungi and grow in parts of Asia, Australasia and the Americas. Siti Munirah Mat Yunoh at the Forest Research Institute Malaysia in Kepong and her colleagues described a new species of Thismia that was first found in 2019 in a Malaysian rain forest. The scientists named the plant Thismia sitimeriamiae after the mother of the local explorer who discovered it, in honour of her support for her son’s nature-conservation efforts.Thismia sitimeriamiae is only about two centimetres tall, and sports an orange flower shaped like a funnel with an umbrella-like structure on top. The plant seems to be so rare that it should be considered critically endangered: just four individuals of T. sitimeriamiae have ever been seen, and wild boars have destroyed all but one of these, the authors say.

PhytoKeys (2021)

Conservation biology More