Philippa Kaur

Hu, C.-H. in Introduction to Roadside Geology of Ten Field Geology Excursion Routes in Northern Taiwan (ed Taiwan Normal University Department of Earth Science) 63–100 (Taiwan Normal University, 1987).Hu, C.-H. Fossil molluscs of Tongxiao Formation (Pleistocene), Longgang area, Miaoli County. Atlas Fossil Mollusca Taiwan 2, 689–754 (1992).
Google Scholar 
Hu, C.-H. Fossil molluscs of Tongxiao Formation (Pleistocene) in Baishatun and Touwo, Tongxiao village, Miaoli County. Atlas Fossil Mollusca Taiwan 1, 175–314 (1991).
Google Scholar 
Hayasaka, I. & Morishita, A. Notes on some fossil echinoids of Taiwan, II. Acta Geol. Taiwan. 1, 93–110 (1947).
Google Scholar 
Lin, Y.-J., Fang, J.-N., Chang, C.-C., Cheng, C.-C. & Lin, J. P. Stereomic microstructure of Clypeasteroida in thin section based on new material from Pleistocene strata in Taiwan. Terr. Atmos. Ocean. Sci. J. https://doi.org/10.3319/TAO.2021.07.28.01 (2021).Article 

Google Scholar 
Morishita, A. in Contributions to Celebrate Prof. Ichiro Hayasaka’s 76th Birthday 109–116 (1967).Wang, C.-C., Lin, C.-F. & Li, L.-C. Measurements on Late Pleistocene sand dollar Scaphechinus mirabilis from northern Taiwan. Annu. Rep. Central Geol. Surv. 72, 49–56 (1984).
Google Scholar 
Nisiyama, S. The echinoid fauna from Japan and adjacent regions. Part 2. Palaeontol. Soc. Jpn. Spec. Pap. 13, 1–491 (1968).
Google Scholar 
Kashenko, S. D. Effects of extreme changes of sea water temperature and salinity on the development of the sand dollar Scaphechinus mirabilis. Russ. J. Mar. Biol. 35, 422–430. https://doi.org/10.1134/s1063074009050083 (2009).Article 

Google Scholar 
Davies, A. J. & John, C. M. The clumped (13C–18O) isotope composition of echinoid calcite: Further evidence for “vital effects” in the clumped isotope proxy. Geochim. Cosmochim. Acta 245, 172–189. https://doi.org/10.1016/j.gca.2018.07.038 (2019).ADS 
CAS 
Article 

Google Scholar 
Chen, W.-S., Yeh, J.-J. & Syu, S.-J. Late Cenozoic exhumation and erosion of the Taiwan orogenic belt: New insights from petrographic analysis of foreland basin sediments and thermochronological dating on the metamorphic orogenic wedge. Tectonophysics 750, 56–69. https://doi.org/10.1016/j.tecto.2018.09.003 (2019).ADS 
Article 

Google Scholar 
Peng, T.-R., Wang, C.-H. & Chen, C. T. A. Oxygen and carbon isotopic studies of fossil Mollusca in the Kuokang Shell Bed, Paishatung, Miaoli. Spec. Publ. Central Geol. Surv. 4, 307–322 (1990).
Google Scholar 
Lee, C.-L. Biostratigraphy and sedimentary environments of Toukoshan Formation in Baishatun area, Miaoli MS thesis, National Central University (2000).Locarnini, R. A. et al. World Ocean Atlas 2018, Volume 1: Temperature. 1–52 (NOAA, 2019).Liew, P.-M. Quaternary stratigraphy in western Taiwan: Palynological correlation. Proc. Geol. Soc. China 31, 169–180 (1988).
Google Scholar 
Siddall, M., Rohling, E. J., Thompson, W. G. & Waelbroeck, C. Marine isotope stage 3 sea level fluctuations: Data synthesis and new outlook. Rev. Geophys. https://doi.org/10.1029/2007rg000226 (2008).Article 

Google Scholar 
LeGrande, A. N. & Schmidt, G. A. Global gridded data set of the oxygen isotopic composition in seawater. Geophys. Res. Lett. https://doi.org/10.1029/2006gl026011 (2006).Article 

Google Scholar 
Waelbroeck, C. et al. Sea-level and deep water temperature changes derived from benthic formainifera isotopic records. Quatern. Sci. Rev. 21, 295–305 (2002).ADS 
Article 

Google Scholar 
Epstein, S., Buchsbaum, R., Lowenstam, H. A. & Urey, H. C. Revised carbonate-water isotopic temperature scale. Bull. Geol. Soc. Am. 64, 1315–1326 (1963).Article 

Google Scholar 
Weber, J. N. & Raup, D. M. Fractionation of the stable isotopes of carbon and oxygen in marine calcareous organisms—the Echinoidea. Part II. Environmental and genetic factors. Geochim. Cosmochim. Acta 30, 705–736 (1966).ADS 
CAS 
Article 

Google Scholar 
Eiler, J. M. Paleoclimate reconstruction using carbonate clumped isotope thermometry. Quatern. Sci. Rev. 30, 3575–3588. https://doi.org/10.1016/j.quascirev.2011.09.001 (2011).ADS 
Article 

Google Scholar 
Takeda, S. Mechanism maintaining dense beds of the sand dollar Scaphechinus mirabilis in northern Japan. J. Exp. Mar. Biol. Ecol. 363, 21–27. https://doi.org/10.1016/j.jembe.2008.06.010 (2008).Article 

Google Scholar 
Takatsu, T., Nakatani, T., Miyamoto, T., Kooka, K. & Takahashi, T. Spatial distribution and feeding habits of Pacific cod (Gadus macrocephalus) larvae in Mutsu Bay, Japan. Fish. Oceanogr. 11, 90–101 (2002).Article 

Google Scholar 
Zhao, M., Huang, C.-Y. & Wei, K.-Y. A 28,000 year U37 K’ sea-surface temperature record of ODP Site 1202B, the southern Okinawa Trough. TAO 16, 45–56 (2005).ADS 
Article 

Google Scholar 
Jan, S., Tseng, Y.-H. & Dietrich, D. E. Sources of water in the Taiwan Strait. J. Oceanogr. 66, 211–221 (2010).Article 

Google Scholar 
Liao, E., Oey, L. Y., Yan, X.-H., Li, L. & Jiang, Y. The deflection of the China Coastal Current over the Taiwan Bank in winter. J. Phys. Oceanogr. 48, 1433–1450. https://doi.org/10.1175/jpo-d-17-0037.1 (2018).ADS 
Article 

Google Scholar 
Hu, J., Kawamura, H., Li, C., Hong, H. & Jiang, Y. Review on current and seawater volume transport through the Taiwan Strait. J. Oceanogr. 66, 591–610 (2010).Article 

Google Scholar 
Pico, T., Mitrovica, J. X., Ferrier, K. L. & Braun, J. Global ice volume during MIS 3 inferred from a sea-level analysis of sedimentary core records in the Yellow River Delta. Quatern. Sci. Rev. 152, 72–79. https://doi.org/10.1016/j.quascirev.2016.09.012 (2016).ADS 
Article 

Google Scholar 
Klein, R. T., Lohmann, K. C. & Kennedy, G. L. Elemental and isotopic proxies of paleotemperature and paleosalinity: Climate reconstruction of the marginal northeast Pacific ca. 80 ka. Geology 25, 363–366 (1997).ADS 
CAS 
Article 

Google Scholar 
Jarvis, I., Trabucho-Alexandre, J., Gröcke, D. R., Uličný, D. & Laurin, J. Intercontinental correlation of organic carbon and carbonate stable isotope records: Evidence of climate and sea-level change during the Turonian (Cretaceous). Depos. Rec. 1, 53–90. https://doi.org/10.1002/dep2.6 (2016).Article 

Google Scholar 
Chen, P. S. M. A study of the stratigraphy and molluscan fossils of the Tunghsiao area, Miaoli, Taiwan, R.O.C.. Bull. Malacol. Republic of China 4, 63–78 (1977).
Google Scholar 
Chen, W.-S. & Hsu, W.-J. The Pleistocene paleoenvironmental significance of the unearthed megafauna strata in Taiwan. Bull. Central Geol. Surv. 23, 137–163 (2010).
Google Scholar 
Chang, C. H. et al. The first archaic Homo from Taiwan. Nat. Commun. 6, 6037. https://doi.org/10.1038/ncomms7037 (2015).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Cai, B.-Q. Fossil human humerus of Late Pleistocene from the Taiwan Straits. Acta Antrhopologica Sinica 20, 178–185 (2001).
Google Scholar 
Tong, H. & Patou-Mathis, M. Mammoth and other proboscideans in China during the Late Pleistocene. Deinsea 9, 421–428 (2003).
Google Scholar 
Koch, P. L. & Barnosky, A. D. Late quaternary extinctions: State of the debate. Annu. Rev. Ecol. Evol. Syst. 37, 215–250. https://doi.org/10.1146/annurev.ecolsys.34.011802.132415 (2006).Article 

Google Scholar 
Brook, B. W. & Bowman, D. M. J. S. Explaining the Pleistocene megafaunal extinctions: Models, chronologies, and assumptions. PNAS 99, 14624–14627 (2002).ADS 
CAS 
Article 

Google Scholar 
Barnosky, A. D., Koch, P. L., Feranec, R. S., Wing, S. L. & Shabel, A. B. Assessing the causes of Late Pleistocene extinctions on the continents. Science 306, 70–75 (2004).ADS 
CAS 
Article 

Google Scholar 
Ugan, A. & Byers, D. A global perspective on the spatiotemporal pattern of the Late Pleistocene human and woolly mammoth radiocarbon record. Quatern. Int. 191, 69–81. https://doi.org/10.1016/j.quaint.2007.09.035 (2008).Article 

Google Scholar 
Adlan, Q., Davies, A. J. & John, C. M. Effects of oxygen plasma ashing treatment on carbonate clumped isotopes. Rapid Commun. Mass Spectrom. 34, e8802. https://doi.org/10.1002/rcm.8802 (2020).CAS 
Article 
PubMed 

Google Scholar 
John, C. M. & Bowen, D. Community software for challenging isotope analysis: First applications of “Easotope” to clumped isotopes. Rapid Commun. Mass Spectrom. 30, 2285–2300 (2016).ADS 
CAS 
Article 

Google Scholar 
Bernasconi, S. M. et al. Background effects on Faraday collectors in gas-source mass spectrometry and implications for clumped isotope measurements. Rapid Commun. Mass Spectrom. 27, 603–612. https://doi.org/10.1002/rcm.6490 (2013).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Bernasconi, S. M. et al. InterCarb: A community effort to improve interlaboratory standardization of the carbonate clumped isotope thermometer using carbonate standards. Geochem. Geophys. Geosyst. 22, e2020GC009588. https://doi.org/10.1029/2020GC009588 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Anderson, N. T. et al. Unified clumped isotope thermometer calibration (0.5–1,100°C) using carbonate-based standardization. Geophys. Res. Lett. 48, e2020GL092069 (2021).ADS 
CAS 
Article 

Google Scholar 
Lee, H. et al. Young colonization history of a widespread sand dollar (Echinodermata; Clypeasteroida) in western Taiwan. Quatern. Int. 528, 120–129 (2019).Article 

Google Scholar 
Reimer, P. J. et al. IntCal13 and Marine13 radiocarbon age calibration curves 0–50,000 years cal BP. Radiocarbon 55, 1869–1887 (2013).CAS 
Article 

Google Scholar  More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More

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

Barbier, E. B. et al. The value of estuarine and coastal ecosystem services. Ecol. Monogr. 81, 169–193 (2011).Article 

Google Scholar 
Lotze, H. K. Depletion, degradation, and recovery potential of estuaries and coastal seas. Science 312, 1806–1809 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Davis, J. & Kidd, I. M. Identifying major stressors: The essential precursor to restoring cultural ecosystem services in a degraded estuary. Estuar. Coast. 35, 1007–1017 (2012).Article 

Google Scholar 
Copeland, B. Effects of decreased river flow on estuarine ecology. J. Water Pollut. Control Fed. 66, 1831–1839 (1966).
Google Scholar 
Mcowen, C. J. et al. A global map of saltmarshes. Biodivers. Data J. https://doi.org/10.3897/BDJ.5.e11764 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Jackson, J. B. C. Historical overfishing and the recent collapse of coastal ecosystems. Science 293, 629–637 (2001).CAS 
PubMed 
Article 

Google Scholar 
McAfee, D. & Connell, S. D. The global fall and rise of oyster reefs. Front. Ecol. Environ. 19, 118–125 (2021).Article 

Google Scholar 
Powers, R. P. & Jetz, W. Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nat. Clim. Chang. 9, 323–329 (2019).ADS 
Article 

Google Scholar 
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Hoekstra, J. M., Boucher, T. M., Ricketts, T. H. & Roberts, C. Confronting a biome crisis: Global disparities of habitat loss and protection. Ecol. Lett. 8, 23–29 (2005).Article 

Google Scholar 
Goldewijk, K. K. Estimating global land use change over the past 300 years: The HYDE database. Glob. Biogeochem. 15, 417–433 (2001).ADS 
Article 

Google Scholar 
Munday, P. L. Habitat loss, resource specialization, and extinction on coral reefs. Glob. Chang. Biol. 10, 1642–1647 (2004).ADS 
Article 

Google Scholar 
Tilman, D., May, R. M., Lehman, C. L. & Nowak, M. A. Habitat destruction and the extinction debt. Nature 371, 65–66 (1994).ADS 
Article 

Google Scholar 
Elliott, M., Burdon, D., Hemingway, K. L. & Apitz, S. E. Estuarine, coastal and marine ecosystem restoration: Confusing management and science—A revision of concepts. Estuar. Coast. Shelf Sci. 74, 349–366 (2007).ADS 
Article 

Google Scholar 
Benayas, J. M. R., Newton, A. C., Diaz, A. & Bullock, J. M. Enhancement of biodiversity and ecosystem services by ecological restoration: A meta-analysis. Science 325, 1121–1124 (2009).ADS 
CAS 
Article 

Google Scholar 
Hobbs, R. J. & Norton, D. A. Towards a conceptual framework for restoration ecology. Restor. Ecol. 4, 93–110 (1996).Article 

Google Scholar 
Jones, C. G., Lawton, J. H. & Shachak, M. Organisms as Ecosystem Engineers in Ecosystem Management 130–147 (Springer, 1994).Tolley, S. G. & Volety, A. K. The role of oysters in habitat use of oyster reefs by resident fishes and decapod crustaceans. J. Shellf. Res. 24, 1007–1012 (2005).Article 

Google Scholar 
Carroll, J. M., Keller, D. A., Furman, B. T. & Stubler, A. D. Rough around the edges: Lessons learned and future directions in marine edge effects studies. Curr. Landsc. Ecol. Rep. 4, 91–102 (2019).Article 

Google Scholar 
Harwell, H. D., Posey, M. H. & Alphin, T. D. Landscape aspects of oyster reefs: Effects of fragmentation on habitat utilization. J. Exp. Mar. Biol. Ecol. 409, 30–41 (2011).Article 

Google Scholar 
Shervette, V. R. & Gelwick, F. Seasonal and spatial variations in fish and macroinvertebrate communities of oyster and adjacent habitats in a Mississippi estuary. Estuar. Coast. 31, 584–596 (2008).Article 

Google Scholar 
Gain, I. E. et al. Macrofauna using intertidal oyster reef varies in relation to position within the estuarine habitat mosaic. Mar. Biol. 164, 1–16 (2017).MathSciNet 
Article 

Google Scholar 
Wong, M., Peterson, C. & Piehler, M. Evaluating estuarine habitats using secondary production as a proxy for food web support. Mar. Ecol. Prog. Ser. 440, 11–25 (2011).ADS 
Article 

Google Scholar 
Meyer, D. L. Habitat partitioning between the xanthid crabs Panopeus herbstii and Eurypanopeus depressus on intertidal oyster reefs (Crassostrea virginica) in southeastern North Carolina. Estuaries 17, 674–679 (1994).Article 

Google Scholar 
McDonald, J. Divergent life history patterns in the co-occurring intertidal crabs Panopeus herbstii and Eurypanopeus depressus (Crustacea: Brachyura: Xanthidae). Mar. Ecol. Prog. Ser. 8, 173–180 (1982).ADS 
Article 

Google Scholar 
Grabowski, J. H. & Peterson, C. H. Restoring oyster reefs to recover ecosystem services in Theoretical Ecology Series vol. 4 281–298 (Elsevier, 2007).Beck, M. W. et al. Oyster reefs at risk and recommendations for conservation, restoration, and management. Bioscience 61, 107–116 (2011).Article 

Google Scholar 
Reeves, S. E. et al. Facilitating better outcomes: how positive species interactions can improve oyster reef restoration. Front. Mar. Sci. 7, 656 (2020).Article 

Google Scholar 
Jud, Z. R. & Layman, C. A. Changes in motile benthic faunal community structure following large-scale oyster reef restoration in a subtropical estuary. Food Webs 25, e00177 (2020).Article 

Google Scholar 
Pinnell, C. M., Ayala, G. S., Patten, M. V. & Boyer, K. E. Seagrass and oyster reef restoration in living shorelines: effects of habitat configuration on invertebrate community assembly. Diversity 13, 246 (2021).Article 

Google Scholar 
La Peyre, M. K., Humphries, A. T., Casas, S. M. & La Peyre, J. F. Temporal variation in development of ecosystem services from oyster reef restoration. Ecol. Eng. 63, 34–44 (2014).Article 

Google Scholar 
Geraldi, N. R., Powers, S. P., Heck, K. L. & Cebrian, J. Can habitat restoration be redundant? Response of mobile fishes and crustaceans to oyster reef restoration in marsh tidal creeks. Mar. Ecol. Prog. Ser. 389, 171–180 (2009).ADS 
Article 

Google Scholar 
Humphries, A. T. & La Peyre, M. K. Oyster reef restoration supports increased nekton biomass and potential commercial fishery value. PeerJ 3, e1111 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Ziegler, S. L., Grabowski, J. H., Baillie, C. J. & Fodrie, F. Effects of landscape setting on oyster reef structure and function largely persist more than a decade post-restoration. Restor. Ecol. 26, 933–942 (2018).Article 

Google Scholar 
Rodriguez, A. B. et al. Oyster reefs can outpace sea-level rise. Nat. Clim. Change 4, 493–497 (2014).ADS 
Article 

Google Scholar 
Hanke, M. H., Posey, M. H. & Alphin, T. D. The influence of habitat characteristics on intertidal oyster Crassostrea virginica populations. Mar. Ecol. Prog. Ser. 571, 121–138 (2017).ADS 
Article 

Google Scholar 
Barber, A., Walters, L. & Birch, A. Potential for restoring biodiversity of macroflora and macrofauna on oyster reefs in Mosquito Lagoon, Florida. Fla. Sci. 66, 47–62 (2010).
Google Scholar 
Boudreaux, M. L., Stiner, J. L. & Walters, L. J. Biodiversity of sessile and motile macrofauna on intertidal oyster reefs in Mosquito Lagoon, Florida. J. Shellf. Res. 25, 1079–1089 (2006).Article 

Google Scholar 
Desmond, J. S., Deutschman, D. H. & Zedler, J. B. Spatial and temporal variation in estuarine fish and invertebrate assemblages: Analysis of an 11-year data set. Estuaries 25, 552–569 (2002).Article 

Google Scholar 
Xu, Y., Xian, W. & Li, W. Spatial and temporal variations of invertebrate community in the Yangtze River Estuary and its adjacent waters. Biodivers. Sci. 22, 311 (2014).Article 

Google Scholar 
Nichols, F. H. Abundance fluctuations among benthic invertebrates in two pacific estuaries. Estuaries 8, 136 (1985).Article 

Google Scholar 
Van Horn, J. & Tolley, S. G. Patterns of distribution along a salinity gradient in the flatback mud crab Eurypanopeus depressus. Gulf Mex. Sci. 26, 66 (2008).
Google Scholar 
Costlow, J. D., Bookhout, C. G. & Monroe, R. Salinity-temperature effects on the larval development of the crab, Panopeus herbstii Milne-Edwards, reared in the laboratory. Physiol. Zool. 35, 79–93 (1962).Article 

Google Scholar 
Sulkin, S., Heukelem, W. & Kelly, P. Behavioral basis for depth regulation in the hatching and post larval stages of the mud crab Eurypanopeus depresus. Mar. Ecol. Prog. Ser. 11, 157–164 (1983).ADS 
Article 

Google Scholar 
Phlips, E. J., Badylak, S. & Grosskopf, T. Factors affecting the abundance of phytoplankton in a restricted subtropical lagoon, the Indian River Lagoon, Florida, USA. Estuar. Coast. Shelf. Sci. 55, 385–402 (2002).ADS 
CAS 
Article 

Google Scholar 
Menendez, R. J. Vertical zonation of the xanthid mud crabs Panopeus obesus and Panopeus simpsoni on oyster reefs. Bull. Mar. Sci. 40, 73–77 (1987).
Google Scholar 
Barshaw, D. E. & Lavalli, K. L. Predation upon postlarval lobsters Homarus americanus by cunners Tautogolabrus adspersus and mud crabs Neopanope sayi on three different substrates: Eelgrass, mud and rocks. Mar. Ecol. Prog. Ser. 48, 119–123 (1988).ADS 
Article 

Google Scholar 
Key, P. B., Wirth, E. F. & Fulton, M. H. A review of grass shrimp, Palaemonetes spp., as a bioindicator of anthropogenic impacts. Environ. Bioindic. 1, 115–128 (2006).CAS 
Article 

Google Scholar 
Kneib, R. T. & Weeks, C. A. Intertidal distribution and feeding habits of the mud crab, Eurytium limosum. Estuaries 13, 462 (1990).Article 

Google Scholar 
Hsueh, P.-W., McClintock, J. B. & Hopkins, T. S. Comparative study of the diets of the blue crabs Callinectes similis and C. sapidus from a mud-bottom habitat in Mobile Bay, Alabama. J. Crust. Biol. 12, 615–619 (1992).Article 

Google Scholar 
King, S. P. & Sheridan, P. Nekton of new seagrass habitats colonizing a subsided salt marsh in Galveston Bay, Texas. Estuar. Coast. 29, 286–296 (2006).Article 

Google Scholar 
Zupo, V. & Nelson, W. Factors influencing the association patterns of Hippolyte zostericola and Palaemonetes intermedius (Decapoda: Natantia) with seagrasses of the Indian River Lagoon, Florida. Mar. Biol. 134, 181–190 (1999).Article 

Google Scholar 
Weber, J. C. & Epifanio, C. E. Response of mud crab (Panopeus herbstii) megalopae to cues from adult habitat. Mar. Biol. 126, 655–661 (1996).Article 

Google Scholar 
Harris, K. P. Oyster Reef Restoration: Impacts on Infaunal Communities in a Shallow Water Estuary. Honors Thesis. University of Central Florida (2018).Shaffer, M., Donnelly, M. & Walters, L. Does intertidal oyster reef restoration affect avian community structure and behavior in a shallow estuarine system? A post-restoration analysis. Fla. Field Nat. 47, 37–59 (2019).
Google Scholar 
Puckett, B. J. et al. Integrating larval dispersal, permitting, and logistical factors within a validated habitat suitability index for oyster restoration. Front. Mar. Sci. 5, 76 (2018).Article 

Google Scholar 
Kim, C., Park, K. & Powers, S. P. Establishing restoration strategy of eastern oyster via a coupled biophysical transport model. Restor. Ecol. 21, 353–362 (2013).Article 

Google Scholar 
Rodriguez-Perez, A., James, M. A. & Sanderson, W. G. A small step or a giant leap: Accounting for settlement delay and dispersal in restoration planning. PLoS ONE 16, e0256369 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yeager, L. A. & Layman, C. A. Energy flow to two abundant consumers in a subtropical oyster reef food web. Aquat. Ecol. 45, 267–277 (2011).Article 

Google Scholar 
Rodney, W. S. & Paynter, K. T. Comparisons of macrofaunal assemblages on restored and non-restored oyster reefs in mesohaline regions of Chesapeake Bay in Maryland. J. Exp. Mar. Biol. Ecol. 335, 39–51 (2006).Article 

Google Scholar 
Macreadie, P. I., Geraldi, N. R. & Peterson, C. H. Preference for feeding at habitat edges declines among juvenile blue crabs as oyster reef patchiness increases and predation risk grows. Mar. Ecol. Prog. Ser. 466, 145–153 (2012).ADS 
Article 

Google Scholar 
Fodrie, F. J. et al. Measuring individuality in habitat use across complex landscapes: Approaches, constraints, and implications for assessing resource specialization. Oecologia 178, 75–87 (2015).ADS 
PubMed 
Article 

Google Scholar 
Grabowski, J. H. et al. Regional environmental variation and local species interactions influence biogeographic structure on oyster reefs. Ecology 101, e02921 (2020).PubMed 
Article 

Google Scholar 
Peterson, C., Grabowski, J. & Powers, S. Estimated enhancement of fish production resulting from restoring oyster reef habitat: Quantitative valuation. Mar. Ecol. Prog. Ser. 264, 249–264 (2003).ADS 
Article 

Google Scholar 
Garvis, S. K., Sacks, P. E. & Walters, L. J. Formation, movement, and restoration of dead intertidal oyster reefs in Canaveral National Seashore and Mosquito Lagoon, Florida. J. Shellf. Res. 34, 251–258 (2015).Article 

Google Scholar 
Gilmore, G. R. Environmental and biogeographic factors influencing ichthyofaunal diversity: Indian River Lagoon. Bull. Mar. Sci. 57, 153–170 (1995).
Google Scholar 
Swain, H. M. Reconciling rarity and representation: A review of listed species in the Indian River Lagoon. Bull. Mar. Sci. 57, 252–266 (1995).ADS 

Google Scholar 
Tremain, D. M. & Adams, D. H. Seasonal variations in species diversity, abundance, and composition of fish communities in the northern Indian River Lagoon, Florida. Bull. Mar. Sci. 57, 171–192 (1995).
Google Scholar 
Paperno, R., Mille, K. & Kadison, E. Patterns in species composition of fish and selected invertebrate assemblages in estuarine subregions near Ponce de Leon Inlet, Florida. Estuar. Coast. Shelf. Sci. 52, 117–130 (2001).ADS 
Article 

Google Scholar 
Smithsonian Marine Station (SMS) at Fort Pierce. Indian River Lagoon Species Inventory https://naturalhistory2.si.edu/smsfp/irlspec/Walters, L. J. et al. A negative association between recruitment of the eastern oyster Crassostrea virginica and the brown tide Aureoumbra lagunensis in Mosquito Lagoon, Florida. Fla. Sci. 84, 81–91 (2021).
Google Scholar 
Walters, L. J., Sacks, P. E. & Campbell, D. E. Boating impacts and boat-wake resilient restoration of the eastern oyster Crassostrea virginica in Mosquito Lagoon, Florida, USA. Fla. Sci. 84, 173–199 (2021).
Google Scholar 
Hanke, M. H., Posey, M. H. & Alphin, T. D. The effects of intertidal oyster reef habitat characteristics on faunal utilization. Mar. Ecol. Prog. Ser. 581, 57–70 (2017).ADS 
Article 

Google Scholar 
Crabtree, R. E. & Dean, J. M. The structure of two South Carolina estuarine tide pool fish assemblages. Estuaries 5, 2–9 (1982).Article 

Google Scholar 
Baggett, L. P. et al. Guidelines for evaluating performance of oyster habitat restoration: Evaluating performance of oyster restoration. Restor. Ecol. 23, 737–745 (2015).Article 

Google Scholar 
Chambers, L. G. et al. How well do restored intertidal oyster reefs support key biogeochemical properties in a coastal lagoon?. Estuar. Coast. 41, 784–799 (2018).Article 

Google Scholar 
Shannon, C. & Wiener, W. The Mathematical Theory of Communication (Illinois Press, 1963).
Google Scholar 
Nagendra, H. Opposite trends in response for the Shannon and Simpson indices of landscape diversity. Appl. Geogr. 22, 175–186 (2002).Article 

Google Scholar 
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., & R Core Team. nlme: Linear and Nonlinear Mixed Effects Models (2021).Hulbert, J. Pseudoreplication and the design of field experiments in ecology. Ecol. Monogr. 54, 187–211 (1984).Article 

Google Scholar 
Wang, Z. & Goonewardene, L. A. The use of MIXED models in the analysis of animal experiments with repeated measures data. Can. J. Anim. Sci. 84, 1–11 (2004).Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Mixed-Effects Models Using the lme4 Package in R (2008).Lenth, R. V. emmeans: Estimated Marginal Means, aka Least-Squares Means (2021).Clarke, K. R., Somerfield, P. J. & Chapman, M. G. On resemblance measures for ecological studies, including taxonomic dissimilarities and a zero-adjusted Bray–Curtis coefficient for denuded assemblages. J. Exp. Mar. Biol. Ecol. 330, 55–80 (2006).Article 

Google Scholar 
Bray, J. R. & Curtis, J. T. An ordination of the upland forest communities of southern Wisconsin. Ecol. Monogr. 27, 325–349 (1957).Article 

Google Scholar 
Anderson, M. J. & Willis, T. J. Canonical analysis of principal coordinates: A useful method of constrained ordination for ecology. Ecology 84, 511–525 (2003).Article 

Google Scholar 
Clarke, K. R., Gorley, R., Somerfield, P. J. & Warwick, R. Change in Marine Communities: An Approach to Statistical Analysis and Interpretation (Primer-E Ltd, 2014).Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Control 19, 716–723 (1974).ADS 
MathSciNet 
MATH 
Article 

Google Scholar 
Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S. (Springer, 2002). More

Dobson, A., Lafferty, K. D., Kuris, A. M., Hechinger, R. F. & Jetz, W. Homage to Linnaeus: How many parasites? How many hosts?. Proc. Natl. Acad. Sci. 105, 11482–11489 (2008).ADS 
CAS 
Article 

Google Scholar 
Kuris, A. M. et al. Ecosystem energetic implications of parasite and free-living biomass in three estuaries. Nature 454, 515–518 (2008).ADS 
CAS 
Article 

Google Scholar 
Seabloom, E. W. et al. The community ecology of pathogens: Coinfection, coexistence and community composition. Ecol. Lett. 18, 401–415 (2015).Article 

Google Scholar 
French, R. K. & Holmes, E. C. An ecosystems perspective on virus evolution and emergence. Trends Microbiol. 28, 165–175 (2020).CAS 
Article 

Google Scholar 
Hudson, P. J., Dobson, A. P. & Lafferty, K. D. Is a healthy ecosystem one that is rich in parasites?. Trends Ecol. Evol. 21, 381–385 (2006).Article 

Google Scholar 
Raffel, T. R., Martin, L. B. & Rohr, J. R. Parasites as predators: Unifying natural enemy ecology. Trends Ecol. Evol. 23, 610–618 (2008).Article 

Google Scholar 
Johnson, P. T. J. et al. When parasites become prey: Ecological and epidemiological significance of eating parasites. Trends Ecol. Evol. 25, 362–371 (2010).Article 

Google Scholar 
Frainer, A., McKie, B. G., Amundsen, P. A., Knudsen, R. & Lafferty, K. D. parasitism and the biodiversity-functioning relationship. Trends Ecol. Evol. 33, 260–268 (2018).Article 

Google Scholar 
Jephcott, T. G., Sime-Ngando, T., Gleason, F. H. & Macarthur, D. J. Host-parasite interactions in food webs: Diversity, stability, and coevolution. Food Webs 6, 1–8 (2016).Article 

Google Scholar 
Rohr, J. R. et al. Towards common ground in the biodiversity–disease debate. Nat. Ecol. Evol. 4, 24–33 (2020).Article 

Google Scholar 
Johnson, P. T. J., De Roode, J. C. & Fenton, A. Why infectious disease research needs community ecology. Science 349, 1259504 (2015).Article 

Google Scholar 
Marcogliese, D. J. & Cone, D. K. Food webs: A plea for parasites. Trends Ecol. Evol. 12, 320–325 (1997).CAS 
Article 

Google Scholar 
Chen, H.-W. et al. Network position of hosts in food webs and their parasite diversity. Oikos 117, 1847–1855 (2008).Article 

Google Scholar 
Lafferty, K. D., Dobson, A. P. & Kuris, A. M. Parasites dominate food web links. Proc. Natl. Acad. Sci. USA 103, 11211–11216 (2006).ADS 
CAS 
Article 

Google Scholar 
Lafferty, K. D. et al. Parasites in food webs: The ultimate missing links. Ecol. Lett. 11, 533–546 (2008).Article 

Google Scholar 
Dunne, J. A. The network structure of food webs. In Ecological Networks: Linking Structure to Dynamics (eds Pascual, M. & Dunne, J. A.) 27–28 (Oxford University Press, 2005).
Google Scholar 
Dunne, J. A., Williams, R. J. & Martinez, N. D. Network structure and biodiversity loss in food webs: Robustness increases with connectance. Ecol. Lett. 5, 558–567 (2002).Article 

Google Scholar 
Hudson, P. J., Rizzoli, A., Grenfell, B. T., Heesterbeek, H. & Dobson, A. P. The Ecology of Wildlife Diseases. (Oxford University Press, Oxford, 2002).
Google Scholar 
Anderson, R. M. & May, R. M. Infectious Diseases of Humans: Dynamics and Control (Oxford University Press, 1992).
Google Scholar 
McCallum, H. & Dobson, A. Detecting disease and parasite threats to endangered species and ecosystems. Trends Ecol. Evol. 10, 190–194 (1995).CAS 
Article 

Google Scholar 
De Castro, F. & Bolker, B. M. Parasite establishment and host extinction in model communities. Oikos 111, 501–513 (2005).Article 

Google Scholar 
McQuaid, C. F. & Britton, N. F. Parasite species richness and its effect on persistence in food webs. J. Theor. Biol. 364, 377–382 (2015).ADS 
Article 

Google Scholar 
Holt, R. D., Dobson, A. P., Begon, M., Bowers, R. G. & Schauber, E. M. Parasite establishment in host communities. Ecol. Lett. 6, 837–842 (2003).
Article 

Google Scholar 
Hatcher, M. J. & Dunn, A. M. Parasites in Ecological Communities: From Interactions to Ecosystems (Cambridge University Press, 2011).Book 

Google Scholar 
Dobson, A. Population dynamics of pathogens with multiple host species. Am. Nat. 164, S64–S78 (2004).Article 

Google Scholar 
McCann, K., Hastings, A. & Huxel, G. R. Weak trophic interactions and the balance of nature. Nature 395, 794–798 (1998).ADS 
CAS 
Article 

Google Scholar 
Neutel, A. M., Heesterbeek, J. A. P. & de Ruiter, P. C. Stability in real food webs: Weak links in long loops. Science 296, 1120–1123 (2002).ADS 
CAS 
Article 

Google Scholar 
Chen, X. & Cohen, J. E. Transient dynamics and food–web complexity in the Lotka–Volterra cascade model. Proc. R. Soc. Lond. Ser. B Biol. Sci. 268, 869–877 (2001).CAS 
Article 

Google Scholar 
May, R. M. Stability in multispecies community models. Math. Biosci. 12, 59–79 (1971).MathSciNet 
Article 

Google Scholar 
May, R. M. Will a large complex system be stable?. Nature 238, 413–414 (1972).ADS 
CAS 
Article 

Google Scholar 
Hilker, F. M. & Schmitz, K. Disease-induced stabilization of predator-prey oscillations. J. Theor. Biol. 255, 299–306 (2008).ADS 
MathSciNet 
Article 

Google Scholar 
Hethcote, H. W., Wang, W., Han, L. & Ma, Z. A predator–prey model with infected prey. Theor. Popul. Biol. 66, 259–268 (2004).Article 

Google Scholar 
Kooi, B. W., van Voorn, G. A. K. & Das, K. P. Stabilization and complex dynamics in a predator-prey model with predator suffering from an infectious disease. Ecol. Complex. 8, 113–122 (2011).Article 

Google Scholar 
Winemiller, K. O. Spatial and temporal variation in tropical fish trophic networks. Ecol. Monogr. 60, 331–367 (1990).Article 

Google Scholar 
Paine, R. T. Food-web analysis through field measurement of per capita interaction strength. Nature 355, 73–75 (1992).ADS 
Article 

Google Scholar 
Wootton, J. T. Estimates and tests of per capita interaction strength: Diet, abundance, and impact of intertidally foraging birds. Ecol. Monogr. 67, 45–64 (1997).Article 

Google Scholar 
Cohen, J. E., Briand, F. & Newman, C. M. Community Food Webs: Data and Theory (Springer, 1990).Book 

Google Scholar 
Mougi, A. Diversity of biological rhythm and food web stability. Biol. Lett. 17, 20200673 (2021).Article 

Google Scholar  More

Sol, D., Bacher, S., Reader, S. M. & Lefebvre, L. Brain size predicts the success of mammal species introduced into novel environments. Am. Nat. 172(Suppl. 1), S63–S71 (2008).PubMed 
Article 

Google Scholar 
González-Lagos, C., Sol, D. & Reader, S. M. Large-brained mammals live longer. J. Evol. Biol. 23, 1064–1074 (2010).PubMed 
Article 

Google Scholar 
Gonda, A., Herczeg, G. & Merilä, J. Evolutionary ecology of intraspecific brain size variation: A review. Ecol. Evol. 3(8), 2751–2764 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Benson-Amram, S., Dantzer, B., Stricker, G., Swanson, E. M. & Holekamp, K. E. Brain size predicts problem-solving ability in mammalian carnivores. PNAS 113(9), 2532–2537 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Näslund, J., Aarestrup, K., Thomassen, S. T. & Johnsson, J. I. Early enrichment effects on brain development in hatchery-reared Atlantic salmon (Salmo salar): No evidence for a critical period. Can. J. Fish. Aquat. Sci. 69(9), 1481–1490 (2012).Article 

Google Scholar 
Logan, C. J., Kruuk, L. E. B., Stanley, R., Thompson, A. M. & Clutton-Brock, T. H. Endocranial volume is heritable and is associated with longevity and fitness in a wild mammal. R. Soc. Open Sci. 3(12), 160622 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yamaguchi, N., Kitchener, A. C., Gilissen, E. & MacDonald, D. W. Brain size of the lion (Panthera leo) and the tiger (P. tigris): Implications for intrageneric phylogeny, intraspecific differences and the effects of captivity. Biol. J. Linn. Soc. 98, 85–93 (2009).Article 

Google Scholar 
Turschwell, M. P. & White, C. R. The effects of laboratory housing and spatial enrichment on brain size and metabolic rate in the eastern mosquitofish Gambusia holbrooki. Biol. Open. 5(3), 205–210 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Welniak-Kaminska, M. et al. Volumes of brain structures in captive wild-type and laboratory rats: 7T magnetic resonance in vivo automatic atlas-based study. PLoS ONE 14(4), e0215348 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Guay, P. J., Parrott, M. & Selwood, L. Captive breeding does not alter brain volume in a marsupial over a few generations. Zoo Biol. 31, 82–86 (2012).PubMed 
Article 

Google Scholar 
Isler, K. et al. Endocranial volumes of primate species: Scaling analyses using a comprehensive and reliable data set. J. Hum. Evol. 55(6), 967–978 (2008).PubMed 
Article 

Google Scholar 
Burns, J. G., Saravanan, A. & Rodd, F. H. Rearing environment affects the brain size of guppies: Lab-reared guppies have smaller brains than wild-caught guppies. Ethol. 115(2), 122–133 (2009).Article 

Google Scholar 
Kruska, D. On the evolutionary significance of encephalization in some eutherian mammals: Effects of adaptive radiation, domestication, and feralization. Brain Behav. Evol. 65(2), 73–108 (2005).PubMed 
Article 

Google Scholar 
Logan, C. J. & Clutton-Brock, T. H. Validating methods for estimating endocranial volume in individual red deer (Cervus elaphus). Behav. Processes. 92, 143–146 (2013).PubMed 
Article 

Google Scholar 
Colby, A. E., Kimock, C. M. & Higham, J. P. Endocranial volume is variable and heritable, but not related to fitness, in a free-ranging primate. Sci. Rep. 11, 1–11 (2021).Article 
CAS 

Google Scholar 
Stuermer, I. W. & Wetzel, W. Early experience and domestication affect auditory discrimination learning, open field behaviour and brain size in wild Mongolian gerbils and domesticated Laboratory gerbils (Meriones unguiculatus forma domestica). Behav. Brain Res. 173, 11–21 (2006).PubMed 
Article 

Google Scholar 
Agnvall, B., Bélteky, J. & Jensen, P. Brain size is reduced by selection for tameness in red junglefowl-correlated effects in vital organs. Sci. Rep. 7(3306), 1–7 (2017).CAS 

Google Scholar 
Röhrs, M. & Ebinger, P. Wild is not really wild: Brain weight of wild and domestic mammals. Berl. Munch. Tierarztliche Wochenschrift. 112(6–7), 234–238 (1999).
Google Scholar 
Smith, B. P., Lucas, T. A., Norris, R. M. & Henneberg, M. Brain size/body weight in the dingo (Canis dingo): Comparisons with domestic and wild canids. Aust. J. Zool. 65(5), 292–301 (2017).Article 

Google Scholar 
Roberts, T., McGreevy, P. & Valenzuela, M. Human induced rotation and reorganization of the brain of domestic dogs. PLoS ONE 5(7), e11946 (2010).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Pollen, A. A. et al. Environmental complexity and social organization sculpt the brain in Lake Tanganyikan cichlid fish. Brain Behav. Evol. 70, 21–39 (2007).PubMed 
Article 

Google Scholar 
Kihslinger, R. L., Lema, S. C. & Nevitt, G. A. Environmental rearing conditions produce forebrain differences in wild Chinook salmon Oncorhynchus tshawytscha. Comp. Biochem. Physiol. 145(2), 145–151 (2006).CAS 
Article 

Google Scholar 
Guay, P. J. & Iwaniuk, A. N. Captive breeding reduces brain volume in waterfowl (Anseriformes). Condor 110(2), 276–284 (2008).Article 

Google Scholar 
Diamond, M. C., Ingham, C. A., Johnson, R. E., Bennett, E. L. & Rosenzweig, M. R. Effects of environment on morphology of rat cerebral cortex and hippocampus. J. Neurobiol. 7, 75–85 (1976).CAS 
PubMed 
Article 

Google Scholar 
Courtney Jones, S. K., Munn, A. J. & Byrne, P. G. Effect of captivity on morphology: Negligible changes in external morphology mask significant changes in internal morphology. R. Soc. Open Sci. 5(5), 1–13 (2018).Article 

Google Scholar 
Kruska, D. & Röhrs, M. Comparative-quantitative investigations on brains of feral pigs from the Galapagos Islands and of European domestic pigs. Z. Anat. Entwicklungsgesch. 144(1), 61–73 (1974).CAS 
PubMed 
Article 

Google Scholar 
Kruska, D. Changes of brain size in Tylopoda during phylogeny and caused by domestication. Verh. Dtsch. Zool. Ges. 75, 173–183 (1982).
Google Scholar 
Groves, C. P. Skull-changes due to captivity in certain Equidae. Z. Säugetierkd. 31, 44–46 (1966).
Google Scholar 
Groves, C. P. The skulls of Asian rhinoceroses: Wild and captive. Zoo Biol. 1, 251–261 (1982).Article 

Google Scholar 
Hollister, N. Some effects of environment and habit on captive lions. Proc. US. Natl. Mus. 53, 177–193 (1917).Article 

Google Scholar 
Price, E. O. Behavioral development in animals undergoing domestication. Appl. Anim. Behav. Sci. 65(3), 245–271 (1999).Article 

Google Scholar 
Wolff, J. Das Gesetz der Transformation der Knochen (A. Hirchwild, 1892).
Google Scholar 
Herring, S. W. Formation of the vertebrate face: Epigenetic and functional influences. Am. Zool. 33, 472–483 (1993).Article 

Google Scholar 
Wroe, S. & Milne, N. Convergence and remarkably consistent constraint in the evolution of carnivore skull shape. Evol. 61(5), 1251–1260 (2007).Article 

Google Scholar 
Damasceno, E. M., Hingst-Zaher, E. & Astúa, D. Bite force and encephalization in the Canidae (Mammalia: Carnivora). J. Zool. 290(4), 246–254 (2013).Article 

Google Scholar 
Van Valkenburgh, B. Deja vu: the evolution of feeding morphologies in the Carnivora. Integr. Comp. Biol. 47, 147–163 (2007).PubMed 
Article 

Google Scholar 
Van Valkenburgh, B. Carnivore dental adaptations and diet: A study of trophic diversity within guilds in Carnivore behavior, ecology, and evolution (ed. Gittleman, J. L.) 410–436 (Springer Science & Business Media, 1989).Slater, G. J., Dumont, E. R. & Van Valkenburgh, B. Implications of predatory specialization for cranial form and function in canids. J. Zool. 278(3), 181–188 (2009).Article 

Google Scholar 
Michaud, M., Veron, G. & Fabre, A. C. Phenotypic integration in feliform carnivores: Covariation patterns and disparity in hypercarnivores versus generalists. Evol. 74(12), 2681–2702 (2020).Article 

Google Scholar 
O’Regan, H. J. & Kitchener, A. C. The effects of captivity on the morphology of captive, domesticated and feral mammals. Mamm. Rev. 35, 215–230 (2005).Article 

Google Scholar 
Kapoor, V., Antonelli, T., Parkinson, J. A. & Hartstone-Rose, A. Oral health correlates of captivity. Res. Vet. Sci. 107, 213–219 (2016).PubMed 
Article 

Google Scholar 
Mitchell, D. R., Wroe, S., Ravosa, M. J. & Menegaz, R. A. More challenging diets sustain feeding performance: Applications toward the captive rearing of wildlife. Integr. Org. Biol. 3, 1–13 (2021).
Google Scholar 
Curtis, A. A., Orke, M., Tetradis, S. & Van Valkenburgh, B. Diet-related differences in craniodental morphology between captive-reared and wild coyotes, Canis latrans (Carnivora: Canidae). Biol. J. Linn. Soc. 123(3), 677–693 (2018).Article 

Google Scholar 
Siciliano-Martina, L., Light, J. E. & Lawing, A. M. Cranial morphology of captive mammals: A meta-analysis. Front. Zool. 18(4), 1–13 (2021).
Google Scholar 
Corruccini, R. S. & Beecher, R. M. Occlusal variation related to soft diet in a nonhuman primate. Science 218, 74–75 (1982).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Ramirez Rozzi, F. V., González-José, R. & Pucciarelli, H. M. Cranial growth in normal and low-protein-fed Saimiri An environmental heterochrony. J. Hum. Evol. 49(4), 515–535 (2005).PubMed 
Article 

Google Scholar 
Taylor, A. B. & van Schaik, C. P. Variation in brain size and ecology in Pongo. J. Hum. Evol. 52, 59–71 (2007).PubMed 
Article 

Google Scholar 
AZA Canid TAG. Large Canid (Canidae) Care Manual. (Association of Zoos and Aquariums, 2012).Mexican Wolf Species Survival Plan. Mexican Gray Wolf Husbandry Manual: Guidelines for Captive Management (2009 edition). (Mexican Wolf Species Survival Plan and U.S. Fish and Wildlife Service, 2009).Carrera, R. et al. Comparison of Mexican wolf and coyote diets in Arizona and New Mexico. The J. Wildl. Manag. 72(2), 376–381 (2008).Article 

Google Scholar 
Reed, J. E. et al. Diets of free-ranging Mexican gray wolves in Arizona and New Mexico. Wildl. Soc. Bull. 34(4), 1127–1133 (2006).Article 

Google Scholar 
Kazimierska, K., Biel, W. & Witkowicz, R. Mineral composition of cereal and cereal-free dry dog foods versus nutritional guidelines. Molecules 25(21), 1–24 (2020).Article 
CAS 

Google Scholar 
Pezzali, J. G. & Aldrich, C. G. Effect of ancient grains and grain-free carbohydrate sources on extrusion parameters and nutrient utilization by dogs. J. Anim. Sci. 98(2), 3758–3767 (2019).Article 

Google Scholar 
Hartstone-Rose, A., Selvey, H., Villari, J. R., Atwell, M. & Schmidt, T. The three-dimensional morphological effects of captivity. PLoS ONE 9(11), 1–15 (2014).Article 
CAS 

Google Scholar 
Siciliano-Martina, L., Light, J. E. & Lawing, A. M. Changes in canid cranial morphology induced by captivity and conservation implications. Biol. Conserv. 257, 109143 (2021).Article 

Google Scholar 
Hedrick, P. W. & Fredrickson, R. Genetic rescue guidelines with examples from Mexican wolves and Florida panthers. Conserv. Genet. 11(2), 615–626 (2010).Article 

Google Scholar 
Greely, S. E. Mexican Wolf, Canis lupus baileyi, International Studbook 2018. Palm Desert, California. (2018).Kalinowski, S. T., Hedrick, P. W. & Miller, P. S. No inbreeding depression observed in Mexican and red wolf captive breeding programs. Conserv. Biol. 13(6), 1371–1377 (1999).Article 

Google Scholar 
Sakai, S. T., Whitt, B., Arsznov, B. M. & Lundrigan, B. L. Endocranial development in the coyote (Canis latrans) and gray wolf (Canis lupus): A computed tomographic study. Brain Behav. Evol. 91(2), 1–18 (2018).Article 

Google Scholar 
Van Valkenburgh, B. Skeletal and dental predictors of body mass in carnivores in Body size in mammalian paleobiology: estimation and biological implications (eds. Damuth, J. & MacFadden, B. J.) (Cambridge University Press, 1990).Rohlf, F. J. TPSDig2: a program for landmark development and analysis (2001).Siciliano-Martina, L., Light, J. E., Riley, D. G. & Lawing, A. M. One of these wolves is not like the other: morphological effects and conservation implications of captivity in Mexican wolves. Anim. Conserv. 25, 77–90 (2021).Article 

Google Scholar 
Zelditch, M. L., Donald, L., Swiderski, H., Sheets, D. & Fink, W. L. Geometric morphometrics for biologists: a primer. (Elsevier Academic Press, 2004).Coster, A. pedigree: Pedigree functions. R package version 1.4 (2013).Traylor-Holzer, K. (ed.). PMx user’s manual. Version 1.0. Apple Valley, MN: IUCN SSC Conservation Breeding Specialist Group. (2011).Thomason, J. J. Cranial strength in relation to estimated biting forces in some Mammals. Can. J. Zool. 69, 2326–2333 (1991).Article 

Google Scholar 
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods. 9(7), 676–682 (2012).CAS 
PubMed 
Article 

Google Scholar 
R Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2020).Cofran, Z. Brain size growth in wild and captive chimpanzees (Pan troglodytes). Am. J. Primat. 80(7), 1–8 (2018).Article 

Google Scholar 
Witzenberger, K. A. & Hochkirch, A. Ex situ conservation genetics: A review of molecular studies on the genetic consequences of captive breeding programmes for endangered animal species. Biodivers. Conserv. 20(9), 1843–1861 (2011).Article 

Google Scholar 
Gómez-Sánchez, D. et al. On the path to extinction: Inbreeding and admixture in a declining grey wolf population. Mole. Ecol. 27(18), 3599–3612 (2018).Article 

Google Scholar 
Elbroch, M. Animal skulls: a guide to North American species. (Stackpole Books, 2006).Conde, D. A., Flesness, N., Colchero, F., Jones, O. R. & Scheuerlein, A. An emerging role of zoos to conserve biodiversity. Science 331, 1390–1391 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Prado, E. L. & Dewey, K. G. Nutrition and brain development in early life. Nutr. Rev. 72(4), 267–284 (2014).PubMed 
Article 

Google Scholar 
Hecht, E. E. et al. Neuromorphological changes following selection for tameness and aggression in the Russian farm-fox experiment. J. Neurosci. 41(28), 6144–6156 (2021).CAS 
PubMed Central 
Article 

Google Scholar 
Bennett, E. L., Rosenzweig, M. R. & Diamond, M. C. Rat brain: Effects of environmental enrichment on wet and dry weights. Science 163(3869), 825–826 (1969).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Cummins, R. A., Walsh, R. N., Budtz-Olsen, O. E., Konstantinos, T. & Horsfall, C. R. Environmentally-induced changes in the brains of elderly rats. Nature 243(5409), 516–518 (1973).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Welch, B. L., Brown, D. G., Welch, A. S. & Lin, D. C. Isolation, restrictive confinement or crowding of rats for one year. I. Weight, nucleic acids and protein of brain regions. Brain Res. 75, 71–84 (1974).CAS 
PubMed 
Article 

Google Scholar  More

Baker, R. W. Membrane Technology and Applications. 3rd edn. ISBN 9780470743720. (Wiley, 2012).Wang, L. K., Chen, J. P., Hung, Y.-T., Shammas, N. K. Membrane and Desalination Technologies. ISBN 978-1-58829-940-6. (Humana Press, 2011).Mahmoudi, E. et al. Enhancing morphology and separation performance of polyamide 6,6 membranes by minimal incorporation of silver decorated graphene oxide nanoparticles. Sci. Rep. 9, 1216. https://doi.org/10.1038/s41598-018-38060-x (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Penkova, A. V., Dmitrenko, M. E., Ermakov, S. S., Toikka, A. M. & Roizard, D. Novel green PVA-fullerenol mixed matrix supported membranes for separating water-THF mixtures by pervaporation. Environ. Sci. Pollut. Res. 25, 20354–20362. https://doi.org/10.1007/s11356-017-9063-9 (2018).CAS 
Article 

Google Scholar 
Atlaskin, A. A. et al. Comprehensive experimental study of membrane cascades type of “continuous membrane column” for gases high-purification. J. Membr. Sci. 572, 92–101. https://doi.org/10.1016/j.memsci.2018.10.079 (2019).CAS 
Article 

Google Scholar 
Koyuncu, I., Sengur, R., Turken, T., Guclu, S., & Pasaoglu, M.E. Advances in water treatment by microfiltration, ultrafiltration, and nanofiltration. in Advances in Membrane Technologies for Water Treatment (eds. Basile, A., Cassano, A., Rastogi, N. K.). 83–128. (Elsevier, 2015).Van der Bruggen, B. Microfiltration, ultrafiltration, nanofiltration, reverse osmosis, and forward osmosis in Fundamental Modelling of Membrane Systems, Membrane and Process Performance (ed. Luis, P.). 25–70. (Elsevier, 2018).Al Aani, S., Mustafa, T. N. & Hilal, N. Ultrafiltration membranes for wastewater and water process engineering: A comprehensive statistical review over the past decade. J. Water Process Eng. 35, 101241. https://doi.org/10.1016/j.jwpe.2020.101241 (2020).Article 

Google Scholar 
Polotskaya, G. A., Goikhman, M. Y., Podeshvo, I. V., Polotsky, A. E. & Cherkasov, A. N. Polybenzoxazinoneimides and their prepolymers as promising membrane materials. Desalination 200, 46–48 (2006).CAS 
Article 

Google Scholar 
Ulbricht, M. Advanced functional polymer membranes. Polymer 47, 2217–2262. https://doi.org/10.1016/j.polymer.2006.01.084 (2006).CAS 
Article 

Google Scholar 
Siagian, U. W. R. et al. High-performance ultrafiltration membrane: Recent progress and its application for wastewater treatment. Curr. Pollut. Rep. 7, 448–462. https://doi.org/10.1007/s40726-021-00204-5 (2021).CAS 
Article 

Google Scholar 
Polotskaya, G. A., Meleshko, T. K., Gofman, I. V., Polotsky, A. E. & Cherkasov, A. N. Polyimide ultrafiltration membranes with high thermal stability and chemical durability. Sep. Sci. Technol. 44, 3814–3831. https://doi.org/10.1080/01496390903256166 (2009).CAS 
Article 

Google Scholar 
Ohya, H., Kudryavtsev, V. V., & Semenova, S. I. Polyimide Membranes. Vol. 314. (Gordon & Breach Publishers, 1996).Liu, R., Qiao, X. & Chung, T.-S. The development of high performance P84 co-polyimide hollow fibers for pervaporation dehydration of isopropanol. Chem. Eng. Sci. 60, 6674–6686. https://doi.org/10.1016/j.ces.2005.05.066 (2005).CAS 
Article 

Google Scholar 
Yang, C. et al. Preparation and characterization of acid and solvent resistant polyimide ultrafiltration membrane. Appl. Surf. Sci. 483, 278–284. https://doi.org/10.1016/j.apsusc.2019.03.226 (2019).ADS 
CAS 
Article 

Google Scholar 
Pulyalina, AYu., Polotskaya, G. A. & Toikka, A. M. Membrane materials based on polyheteroarylenes and their application for pervaporation. Russ. Chem. Rev. 85(1), 81–98 (2016).ADS 
CAS 
Article 

Google Scholar 
Volgin, I. V. et al. Transport properties of thermoplastic R-BAPB polyimide: Molecular dynamics simulations and experiment. Polymers 11, 1775. https://doi.org/10.3390/polym11111775 (2019).CAS 
Article 
PubMed Central 

Google Scholar 
Pulyalina, A. et al. Preparation and characterization of methanol selective membranes based on polyheteroarylene–Cu(I) complexes for purification of methyl tertiary butyl ether. Polym. Int. 66(12), 1873–1882. https://doi.org/10.1002/pi.5463 (2017).CAS 
Article 

Google Scholar 
Pulyalina, A. et al. Sorption and transport of aqueous isopropanol solutions in polyimide-poly(aniline-co-anthranilic acid) composites. Russ. J. Appl. Chem. 84(5), 840–846. https://doi.org/10.1134/S107042721105017X (2011).CAS 
Article 

Google Scholar 
Pulyalina, A. et al. Novel approach to determination of sorption in pervaporation process: A case study of isopropanol dehydration by polyamidoimide urea membranes. Sci. Rep. 7, 8415. https://doi.org/10.1038/s41598-017-08420-0 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Helali, N., Shamaei, L., Rastgar, M. & Sadrzadeh, M. Development of layer-by-layer assembled polyamide-imide membranes for oil sands produced water treatment. Sci. Rep. 11, 8098. https://doi.org/10.1038/s41598-021-87601-4 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Polotskaya, G. et al. Asymmetric membranes based on copolyheteroarylenes with imide, biquinoline, and oxazinone units: Formation and characterization. Polymers 11(10), 1542. https://doi.org/10.3390/polym11101542 (2019).CAS 
Article 
PubMed Central 

Google Scholar 
Volgin, I. V. Transport properties of thermoplastic R-BAPB polyimide: Molecular dynamics simulations and experiment. Polymers 11(11), 1775. https://doi.org/10.3390/polym11111775 (2019).CAS 
Article 
PubMed Central 

Google Scholar 
White, L. S. Transport properties of a polyimide solvent resistant nanofiltration membrane. J. Membr. Sci. 205, 191–202 (2005).Article 

Google Scholar 
Barsema, J. N., Kapantaidakis, G. C., van der Vegt, N. F. A., Koops, G. H. & Wessling, M. Preparation and characterization of highly selective dense and hollow fiber asymmetric membranes based on BTDA-TDI/MDI co-polyimide. J. Membr. Sci. 216, 195–205. https://doi.org/10.1016/S0376-7388(03)00071-1 (2003).CAS 
Article 

Google Scholar 
Pulyalina, A., Polotskaya, G., Rostovtseva, V., Pientka, Z. & Toikka, A. Improved hydrogen separation using hybrid membrane composed of nanodiamonds and P84 copolyimide. Polymers 10, 828. https://doi.org/10.3390/polym10080828 (2018).CAS 
Article 
PubMed Central 

Google Scholar 
Qiao, X. & Chung, T.-S. Fundamental characteristics of sorption, swelling, and permeation of P84 co-polyimide membranes for pervaporation dehydration of alcohols. Ind. Eng. Chem. Res. 44, 8938–8943. https://doi.org/10.1021/ie050836g (2005).CAS 
Article 

Google Scholar 
Mangindaan, D. W., Shi, G. M. & Chung, T.-S. Pervaporation dehydration of acetone using P84 co-polyimide flat sheet membranes modified by vapor phase crosslinking. J. Membr. Sci. 458, 76–85 (2014).CAS 
Article 

Google Scholar 
Hua, D., Ong, Y. K., Wang, Y., Yang, T. & Chung, T.-S. ZIF-90/P84 mixed matrix membranes for pervaporation dehydration of isopropanol. J. Membr. Sci. 453, 155–167. https://doi.org/10.1016/j.memsci.2013.10.059 (2014).CAS 
Article 

Google Scholar 
Pulyalina, AYu., Putintseva, M. N., Polotskaya, G. A., Rostovtseva, V. A. & Toikka, A. M. Pervaporation purification of oxygenate from an ethyl tert-butyl ether/ethanol azeotropic mixture. Membr. Membr. Technol. 1(2), 99–106. https://doi.org/10.1134/S2517751619020082 (2019).CAS 
Article 

Google Scholar 
Ren, J. & Li, Z. Development of asymmetric BTDA-TDI/MDI (P84) copolyimide flat sheet and hollow fiber membranes for ultrafiltration: Morphology transition and membrane performance. Desalination 285, 336–344. https://doi.org/10.1016/j.desal.2011.10.024 (2012).CAS 
Article 

Google Scholar 
Ren, J., Li, Z., Wong, F.-S. & Li, D. Development of asymmetric BTDA-TDI/MDI (P84) co-polyimide hollow fiber membranes for ultrafiltration: The influence of shear rate and approaching ratio on membrane morphology and performance. J. Membr. Sci. 248, 177–188. https://doi.org/10.1016/j.memsci.2004.09.031 (2005).CAS 
Article 

Google Scholar 
Yusoff, I. I. et al. Durable pressure filtration membranes based on polyaniline-polyimide P84 blends. Polym. Eng. Sci. 5(S1), E82–E92 (2019).Article 

Google Scholar 
Grosso, V. et al. Polymeric and mixed matrix polyimide membranes. Sep. Purif. Technol. 132, 684–696 (2014).CAS 
Article 

Google Scholar 
Renner, R. Ionic liquids: An industrial cleanup solution. Environ. Sci. Technol. 35, 410a–413a (2001).ADS 
CAS 
Article 

Google Scholar 
Wanga, H. H. A novel green solvent alternative for polymeric membrane preparation via nonsolvent-induced phase separation (NIPS). J. Membr. Sci. 574, 44–54 (2019).Article 

Google Scholar 
Lessan, F. & Foudazi, R. Effect of [EMIM][BF4] ionic liquid on the properties of ultrafiltration membranes. Polymer 210, 122977 (2020).CAS 
Article 

Google Scholar 
Xing, D. Y., Peng, N. & Chung, T.-S. Formation of cellulose acetate membranes via phase inversion using ionic liquid, [BMIM]SCN, as the solvent. Ind. Eng. Chem. Res. 49, 8761–8769 (2010).CAS 
Article 

Google Scholar 
Durmaz, E. N. & Çulfaz-Emecen, P. Z. Cellulose-based membranes via phase inversion using [EMIM]OAc-DMSO mixtures as solvent. Chem. Eng. Sci. 178, 93–103 (2018).CAS 
Article 

Google Scholar 
Grekov, K. B. Electronic Waste and Safety Problems (in Rus.). ISBN 9785891601796. (SUT, 2018).Svittsov, A. A. & Abylgaziev, TZh. Micellarly enhanced (reagent) ultrafiltration. Russ. Chem. Rev. 60(11), 1280–1283 (1991).ADS 
Article 

Google Scholar 
Petrov, S. & Stoichev, P. A. Reagent ultrafiltration purification of water contaminated with reactive dyes. Filtr. Sep. https://doi.org/10.1016/S0015-1882(02)80229-4 (2002).Article 

Google Scholar 
Leonard, M. A. & West, T. S. Chelating reactions of 1,2-dihydroxyanthraquinon-3-ylmethyl-amine-NN-diacetic acid with metal cations in aqueous media. J. Chem. Soc. 866, 4477–4486. https://doi.org/10.1039/jr9600004477 (1960).Article 

Google Scholar 
Marczenko, Z., & Balcerzak, M. Fluorine. in Separation, Preconcentration and Spectrophotometry in Inorganic Analysis (ed. Kloczko, E.). 189–197. (Elsevier, 2000).Chai, J.-D. & Head-Gordon, M. Long-range corrected hybrid density functionals with damped atom–atom dispersion corrections. Phys. Chem. Chem. Phys. 10, 6615–6620. https://doi.org/10.1039/B810189B (2008).CAS 
Article 
PubMed 

Google Scholar 
Frisch, M. J. et al. Gaussian 09, Revision C.01. (Gaussian, Inc., 2010).Ochterski, J. W. Thermochemistry in Gaussian. https://gaussian.com/thermo/ (2000).Cherkasov, A. N. A rapid analysis of ultrafiltration membrane structure. Sep. Sci. Tech. 40, 2775–2801. https://doi.org/10.1080/01496390500333111 (2005).CAS 
Article 

Google Scholar 
Zheng, Q.-Z. The relationship between porosity and kinetics parameter of membrane formation in PSF ultrafiltration membrane. J. Membr. Sci. 286, 7–11 (2006).CAS 
Article 

Google Scholar 
Barton, A. F. M. CRC Handbook of Solubility Parameter. Vol. 768. (CRC Press, 1991).Tan, X. & Rodrigue, D. A review on porous polymeric membrane preparation. Part I: Production techniques with polysulfone and poly (vinylidene fluoride). Polymers 11, 1160 (2019).Article 

Google Scholar 
Mulder, M. H. V. Phase Inversion Membranes. Membrane Preparation. Vol. 3331. (Academic Press, 2000).Quijada-Maldonado, E. Pilot plant study on the extractive distillation of toluene–methylcyclohexane mixtures using NMP and the ionic liquid [hmim][TCB] as solvents. Sep. Purif. Technol. 166, 196–204 (2016).CAS 
Article 

Google Scholar 
Polotskaya, G. A. et al. Aromatic copolyamides with anthrazoline units in the backbone: Synthesis, characterization, pervaporation application. Polymers 8(10), 362. https://doi.org/10.3390/polym8100362 (2016).CAS 
Article 
PubMed Central 

Google Scholar 
Mao, J. X. Interactions in 1-ethyl-3-methyl imidazolium tetracyanoborate ion pair: Spectroscopic and density functional study. J. Mol. Struct. 1038, 12–18 (2013).ADS 
CAS 
Article 

Google Scholar  More

Wedding, L. M. et al. From principles to practice: a spatial approach to systematic conservation planning in the deep sea. Proc. R. Soc. B Biol. Sci. 280, 20131684 (2013).CAS 
Article 

Google Scholar 
Kaiser, S., Smith, C. R. & MartínezArbizu, P. Editorial: Biodiversity of the Clarion Clipperton Fracture Zone. Mar. Biodivers. 47, 259–264 (2017).Article 

Google Scholar 
Bluhm, H. Monitoring megabenthic communities in abyssal manganese nodule sites of the East Pacific Ocean in association with commercial deep-sea mining. Aquat. Conserv. Mar. Freshw. Ecosyst. 4, 187–201 (1994).Article 

Google Scholar 
Simon-Lledó, E. et al. Multi-scale variations in invertebrate and fish megafauna in the mid-eastern Clarion Clipperton Zone. Prog. Oceanogr. 187, 102405 (2020).Article 

Google Scholar 
Simon-Lledó, E. et al. Megafaunal variation in the abyssal landscape of the Clarion Clipperton Zone. Prog. Oceanogr. 170, 119–133 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hein, J. R., Mizell, K., Koschinsky, A. & Conrad, T. A. Deep-ocean mineral deposits as a source of critical metals for high- and green-technology applications: Comparison with land-based resources. Ore Geol. Rev. 51, 1–14 (2013).Article 

Google Scholar 
Kuhn, T., Wegorzewski, A., Rühlemann, C. & Vink, A. Composition, formation, and occurrence of polymetallic nodules. In Deep-Sea Mining: Resource Potential Technical and Environmental Considerations (ed. Sharma, R.) 23–63 (Springer, 2017). https://doi.org/10.1007/978-3-319-52557-0_2.Chapter 

Google Scholar 
Simon-Lledó, E. et al. Ecology of a polymetallic nodule occurrence gradient: Implications for deep-sea mining. Limnol. Oceanogr. 64, 1883–1894 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
International Seabed Authority. Deep Seabed Minerals Contractors. https://www.isa.org.jm/deep-seabed-minerals-contractors?qt-contractors_tabs_alt=0#qt-contractors_tabs_alt (2020).Jones, D. O. B. et al. Biological responses to disturbance from simulated deep-sea polymetallic nodule mining. PLoS ONE 12, e0171750 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Niner, H. J. et al. Deep-sea mining with no net loss of biodiversity: An impossible aim. Front. Mar. Sci. 5, 53 (2018).ADS 
Article 

Google Scholar 
Kuhn, T., Uhlenkott, K., Vink, A., Rühlemann, C. & MartínezArbizu, P. Manganese nodule fields from the Northeast Pacific as benthic habitats. In Seafloor Geomorphology as Benthic Habitat: GeoHab Atlas of Seafloor Geomorphic Features and Benthic Habitats (eds Harris, P. T. & Baker, E.) 933–947 (Elsevier, 2020).Chapter 

Google Scholar 
Vanreusel, A., Hilario, A., Ribeiro, P. A., Menot, L. & Martínez Arbizu, P. Threatened by mining, polymetallic nodules are required to preserve abyssal epifauna. Sci. Rep. 6, 26808 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Amon, D. J. et al. Insights into the abundance and diversity of abyssal megafauna in a polymetallic-nodule region in the eastern Clarion-Clipperton Zone. Sci. Rep. 6, 30492 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
De Forges, B. R., Koslow, J. A. & Poore, G. C. B. Diversity and endemism of the benthic seamount fauna in the southwest Pacific. Nature 405, 944–947 (2000).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Lodge, M. et al. Seabed mining: International Seabed Authority environmental management plan for the Clarion-Clipperton Zone: A partnership approach. Mar. Policy 49, 66–72 (2014).Article 

Google Scholar 
Cuvelier, D. et al. Are seamounts refuge areas for fauna from polymetallic nodule fields?. Biogeosciences 17, 2657–2680 (2020).ADS 
Article 

Google Scholar 
Wedding, L. M. et al. Managing mining of the deep seabed. Science 349, 144–145 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
International Seabed Authority. Decision of the Council of the International Seabed Authority relating to amendments to the Regulations on the Prospecting and Exploration for Polymetallic Nodules in the Area and related matters. (2013).International Seabed Authority. Recommendations for the guidance of contractors for the assessment of the possible environmental impacts arising from exploration for marine minerals in the Area. (2020).Jones, D. O. B., Ardron, J. A., Colaço, A. & Durden, J. M. Environmental considerations for impact and preservation reference zones for deep-sea polymetallic nodule mining. Mar. Policy 118, 103312 (2020).Article 

Google Scholar 
Uhlenkott, K., Vink, A., Kuhn, T. & Martínez Arbizu, P. Predicting meiofauna abundance to define preservation and impact zones in a deep-sea mining context using random forest modelling. J. Appl. Ecol. 57, 1210–1221 (2020).Article 

Google Scholar 
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).MATH 
Article 

Google Scholar 
Ostmann, A. & Martínez Arbizu, P. Predictive models using random forest regression for distribution patterns of meiofauna in Icelandic waters. Mar. Biodivers. 48, 719–735 (2018).Article 

Google Scholar 
Uhlenkott, K., Vink, A., Kuhn, T., Gillard, B. & Martínez Arbizu, P. Meiofauna in a potential deep-sea mining area: Influence of temporal and spatial variability on small scale abundance models. Diversity 13, 3 (2021).CAS 
Article 

Google Scholar 
Gazis, I.-Z., Schoening, T., Alevizos, E. & Greinert, J. Quantitative mapping and predictive modeling of Mn nodules’ distribution from hydroacoustic and optical AUV data linked by random forests machine learning. Biogeosciences 15, 7347–7377 (2018).ADS 
Article 

Google Scholar 
Ellis, N., Smith, S. J. & Pitcher, C. R. Gradient forests: Calculating importance gradients on physical predictors. Ecology 93, 156–168 (2012).PubMed 
Article 

Google Scholar 
Miljutina, M. A., Miljutin, D. M., Mahatma, R. & Galéron, J. Deep-sea nematode assemblages of the Clarion-Clipperton Nodule Province (Tropical North-Eastern Pacific). Mar. Biodivers. 40, 1–15 (2010).Article 

Google Scholar 
Miljutin, D., Miljutina, M. & Messié, M. Changes in abundance and community structure of nematodes from the abyssal polymetallic nodule field, Tropical Northeast Pacific. Deep Sea Res. Oceanogr. Res. Pap. 106, 126–135 (2015).ADS 
Article 

Google Scholar 
Pape, E., Bezerra, T. N., Hauquier, F. & Vanreusel, A. Limited spatial and temporal variability in meiofauna and nematode communities at distant but environmentally similar sites in an area of interest for deep-sea mining. Front. Mar. Sci. 4, 205 (2017).Article 

Google Scholar 
Hauquier, F. et al. Distribution of free-living marine nematodes in the Clarion-Clipperton Zone: Implications for future deep-sea mining scenarios. Biogeosciences 16, 3475–3489 (2019).ADS 
CAS 
Article 

Google Scholar 
Uhlenkott, K., Vink, A., Kuhn, T. & Martínez Arbizu, P. Meiofauna abundance and distribution predicted with random forest regression in the German exploration area for polymetallic nodule mining, Clarion Clipperton Fracture Zone, Pacific. (2020).Calinski, T. & Harabasz, J. A dendrite method for cluster analysis. Commun. Stat. Theory Methods 3, 1–27 (1974).MathSciNet 
MATH 
Article 

Google Scholar 
Thiel, H. et al. The large-scale environmental impact experiment DISCOL: Reflection and foresight. Deep Sea Res. 48, 3869–3882 (2001).ADS 
Article 

Google Scholar 
Brown, A., Wright, R., Mevenkamp, L. & Hauton, C. A comparative experimental approach to ecotoxicology in shallow-water and deep-sea holothurians suggests similar behavioural responses. Aquat. Toxicol. 191, 10–16 (2017).CAS 
PubMed 
Article 

Google Scholar 
McClain, C. R. Seamounts: identity crisis or split personality?. J. Biogeogr. 34, 2001–2008 (2007).Article 

Google Scholar 
Rogers, A. D. The biology of seamounts: 25 years on. In Advances in Marine Biology Vol. 79 (ed. Sheppard, C.) 137–224 (Academic Press, 2018).
Google Scholar 
Durden, J. M., Bett, B. J., Jones, D. O. B., Huvenne, V. A. I. & Ruhl, H. A. Abyssal hills–hidden source of increased habitat heterogeneity, benthic megafaunal biomass and diversity in the deep sea. Prog. Oceanogr. 137, 209–218 (2015).ADS 
Article 

Google Scholar 
Durden, J. M. et al. Megafaunal ecology of the western Clarion Clipperton Zone. Front. Mar. Sci. 8, 671062 (2021).ADS 
Article 

Google Scholar 
Jones, D. O. B. et al. Environment, ecology, and potential effectiveness of an area protected from deep-sea mining (Clarion Clipperton Zone, abyssal Pacific). Prog. Oceanogr. 197, 102653 (2021).Article 

Google Scholar 
Lutz, M. J., Caldeira, K., Dunbar, R. B. & Behrenfeld, M. J. Seasonal rhythms of net primary production and particulate organic carbon flux to depth describe the efficiency of biological pump in the global ocean. J. Geophys. Res. Oceans 112, C10011 (2007).ADS 
Article 
CAS 

Google Scholar 
Volz, J. B. et al. Natural spatial variability of depositional conditions, biogeochemical processes and element fluxes in sediments of the eastern Clarion-Clipperton Zone. Pacific Ocean. Deep Sea Res. 140, 159–172 (2018).CAS 
Article 

Google Scholar 
Smith, C. R., De Leo, F. C., Bernardino, A. F., Sweetman, A. K. & Martínez Arbizu, P. Abyssal food limitation, ecosystem structure and climate change. Trends Ecol. Evol. 23, 518–528 (2008).PubMed 
Article 

Google Scholar 
Ramirez-Llodra, E. et al. Deep, diverse and definitely different: unique attributes of the world’s largest ecosystem. Biogeosciences 7, 2851–2899 (2010).ADS 
Article 

Google Scholar 
Kharbush, J. J. et al. Particulate organic carbon deconstructed: Molecular and chemical composition of particulate organic carbon in the ocean. Front. Mar. Sci. 7, 518 (2020).Article 

Google Scholar 
Smith, C. R. et al. Latitudinal variations in benthic processes in the abyssal equatorial Pacific: Control by biogenic particle flux. Deep Sea Res. 44, 2295–2317 (1997).ADS 
CAS 
Article 

Google Scholar 
Kuhn, T. & Rühlemann, C. Exploration of polymetallic nodules and resource assessment: A case study from the German contract area in the Clarion-Clipperton Zone of the tropical Northeast Pacific. Minerals 11, 618 (2021).ADS 
CAS 
Article 

Google Scholar 
Christodoulou, M. et al. Unexpected high abyssal ophiuroid diversity in polymetallic nodule fields of the northeast Pacific Ocean and implications for conservation. Biogeosciences 17, 1845–1876 (2020).ADS 
CAS 
Article 

Google Scholar 
Valavi, R., Elith, J., Lahoz-Monfort, J. J. & Guillera-Arroita, G. Modelling species presence-only data with random forests. Ecography 44, 1731–1742 (2021).Article 

Google Scholar 
Wiedicke-Hombach, M. & Shipboard Scientific Party. Campaign “MANGAN 2008” with R/V Kilo Moana. (2009).Hijmans, R. J. raster: Geographic Data Analysis and Modeling. (2017).Kaufman, L. & Rousseeuw, P. J. Clustering Large Applications (Program CLARA). in Finding Groups in Data 126–163 (Wiley, 1990). https://doi.org/10.1002/9780470316801.ch3.Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M. & Hornik, K. cluster. (2019).Langenkämper, D., Zurowietz, M., Schoening, T. & Nattkemper, T. W. BIIGLE 2.0: Browsing and annotating large marine image collections. Front. Mar. Sci. 4, 83 (2017).Article 

Google Scholar 
Simon-Lledó, E. et al. Preliminary observations of the abyssal megafauna of Kiribati. Front. Mar. Sci. 6, 605 (2019).Article 

Google Scholar 
Amon, D. J. et al. Megafauna of the UKSRL exploration contract area and eastern Clarion-Clipperton Zone in the Pacific Ocean: Annelida, Arthropoda, Bryozoa, Chordata, Ctenophora, Mollusca. Biodivers. Data J. 5, e14598 (2017).Article 

Google Scholar 
Molodtsova, T. N. & Opresko, D. M. Black corals (Anthozoa: Antipatharia) of the Clarion-Clipperton Fracture Zone. Mar. Biodivers. 47, 349–365 (2017).Article 

Google Scholar 
Kersken, D., Janussen, D. & MartínezArbizu, P. Deep-sea glass sponges (Hexactinellida) from polymetallic nodule fields in the Clarion-Clipperton Fracture Zone (CCFZ), northeastern Pacific: Part II—Hexasterophora. Mar. Biodivers. 49, 947–987 (2019).Article 

Google Scholar 
Horton, T. et al. Recommendations for the standardisation of open taxonomic nomenclature for image-based identifications. Front. Mar. Sci. 8, 620702 (2021).Article 

Google Scholar 
Hughes, J. A. & Gooday, A. J. Associations between living benthic foraminifera and dead tests of Syringammina fragilissima (Xenophyophorea) in the Darwin Mounds region (NE Atlantic). Deep Sea Res. 51, 1741–1758 (2004).Article 

Google Scholar 
Liaw, A. & Wiener, M. Classification and regression by random Forest. R News 2, 18–22 (2002).
Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. (2019).R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2019).Wickham, H. Reshaping data with the reshape package. J. Stat. Softw. 21, 1–20 (2007).Article 

Google Scholar 
Wickham, H. The split-apply-combine strategy for data analysis. J. Stat. Softw. 40, 1–29 (2011).
Google Scholar 
Garnier, S. viridisLite: Default Color Maps from ‘matplotlib’ (Lite Version). (2018).Rabosky, A. R. D. et al. Coral snakes predict the evolution of mimicry across New World snakes. Nat. Commun. 7, 1–9 (2016).
Google Scholar 
Smith, M. R. Ternary: An R package for creating ternary plots. Zenodo https://doi.org/10.5281/zenodo.1068996 (2017). More