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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. 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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. 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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.

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CORRESPONDENCE
06 July 2021

Protect pollinators — reform pesticide regulations

Adrian Fisher

 ORCID: http://orcid.org/0000-0001-5300-1910

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Adrian Fisher

Arizona State University, Tempe, Arizona, USA. On behalf of 14 co-signatories.

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Many approved pesticides still damage pollinator health at doses used in agriculture (see, for example, A. R. Main et al. Agric. Ecosyst. Environ. 287, 106693; 2020). We argue that this is due to a systemic failure in pesticide regulations (see, for instance, S. López-Cubillos et al. Nature 573, 196; 2019) that has been exacerbated by weak enforcement. Stricter laws are needed that are evidence-based, override vested interests and recognize pollinators as essential contributors to food security.Policymakers must learn from failures in neonicotinoid regulation (see, for example, F. Sgolastra et al. Biol. Conserv. 241, 108356; 2020). Before approval, pesticide risk assessment should incorporate protocols that address sub-lethal effects on pollinators. These include alterations in their behaviour and fitness under ecologically realistic conditions; mandatory testing on diverse species of native pollinators and of colonies for eusocial pollinators; and toxicity evaluation when combined with other chemicals such as proprietary additives, co-occurring pesticides and environmental residues.Long-term monitoring after approval by appropriate governmental organizations will be necessary to pick up unforeseen environmental interactions promptly.

Nature 595, 172 (2021)
doi: https://doi.org/10.1038/d41586-021-01818-xA full list of co-signatories to this letter appears in Supplementary Information.

Supplementary Information

List of co-signatories

Competing Interests
The authors declare no competing interests.

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