Philippa Kaur

DataAll data was processed and analyzed using R (R Core Team, Version 4.0.3).Dengue case data were collected and shared by the Alcaldía de Medellín, Secretaría de Salud. In Medellin, dengue case surveillance is conducted by public health institutions that classify and report all cases that meet the WHO clinical dengue case criteria for a probable case to Medellin’s Secretaría de Salud through SIVIGILA (“el Sistema Nacional de Vigilancia en Salud Publica). All case data were de-identified and aggregated to the SIT Zone level.Human public transit usage and movement data were collected and shared by the Área Metropolitana del Valle de Aburrá for 50–200 respondents per SIT Zone. The “Encuestas Origen Destino” (Origen Destination Surveys) were conducted in 2005, 2011, and 2016 and published in 2006, 2012, and 2017, with survey methods described by the Área Metropolitana del Valle de Aburrá25. Survey respondents include a randomly selected subset of all Medellin residents in each SIT zone regardless of whether they use public transit or not. Survey respondents reported the start and end locations, purpose for travel, and mode of travel for all movement over the last 24 h from the time the survey was administered. Respondents reported all modes of movement, including public transit, private transit, and movement on foot. The results of the survey published in 2017 are published online by the Área Metropolitana del Valle de Aburrá26, and select data are available through the geodata-Medellin open data portal27. The results and data of the survey published in 2012 are not publicly available and were obtained directly from the Área Metropolitana del Valle de Aburrá.The public transit usage survey data were also used to extract socioeconomic data to the SIT zone; surveyors also reported basic demographic data including household Estrato, which was averaged per SIT zone to estimate zone socioeconomic status. “Estrato” measures socioeconomic status on a scale from 1 (lowest) to 6 (highest). This system is used by the government of Colombia to allocate public services and subsidies (Law 142, 1994). Data from the public transit usage survey were used to extract socioeconomic status data because it is the only location available where the spatial scale of the data matched the spatial scale of the SIT zone.Data on the location of Medellín public transit lines was downloaded as shape files from the geodata-Medellín open data portal27 and subset for each year to the set of transit lines that was available in that year. Data on the opening date of each Medellín public transit line was taken from the Medellín metro website28.Because census data at the zone level were not available for this study and only exists for 2005 and 2018, we used population estimates for each year downloaded from the WorldPop project29 and aggregated by SIT zone. The accuracy of WorldPop estimates were checked against available census data for 2005 and 2018 at the comuna level, accessed via the geodata- Medellín open data portal27.Ethical considerationsNo human subjects research was conducted. All data used was de-identified, and the analysis was conducted on a database of cases meeting the clinical criteria for dengue with no intervention or modification of biological, physical, psychological, or social variables. All methods were performed in accordance with the relevant guidelines and regulations.Data analysisQuantifying public transit usage and distance from nearest transit lineTo quantify public transit usage, we determined if each respondent reported using the metro, metroplus, or ruta alimentadora (supplementary bus route system integrated with the metro system) in the last 24 h. We then calculated the percent of respondents using the public transit system at least once for each SIT zone.To quantify the distance to the nearest public transit line, we calculated the distance from the center point of each zone to the closest metro, metroplus, tranvía, metrocable, ruta alimentadora, or escalera eléctrica. This was recalculated for each year, including new transit lines that were added within that year.Spatial autoregressive models of dengue incidenceDengue incidence per year at the level of the SIT zone was modeled using a fixed effects spatial panel model by maximum likelihood (R package splm30) as described in31. Our fixed effects were socioeconomic status, distance from public transit, a two-way interaction between these factors, and year. To weight dengue cases by population per SIT zone, the model contained a log offset of population per zone per year. Dengue case counts were log transformed after adding one to account for zones with zero dengue cases in a given year. Year was analyzed as a categorical variable to avoid smoothing epidemic years. All continuous variables were scaled to enable comparison of effect size. Because these panel models require balanced data across time, data was truncated to SIT zones that had data for all years available (247 remaining of 291). Spatial dependency was evaluated, and the model was selected using the Hausman specification test and locally robust panel Lagrange Multiplier tests for spatial dependence. Based on a significant Hausman specification test result, which indicates a poor specification of the random effect model, a fixed effect model was chosen. This result is supported by the fact that we had a nearly exhaustive sample of SIT zones in the Medellin metro area. Lagrange multiplier tests were used to determine the most appropriate spatial dependency specifications. Based on the results of the Lagrange multiplier tests, a Spatial Autoregressive (SAR) model was the most appropriate to incorporate spatial dependency; a SAR model considers that the number of dengue cases in a SIT zone depends on the number in neighboring zones.Because public transit usage was a measurement taken during just two of the study years, we constructed an additional fixed effects spatial panel model by maximum likelihood model of dengue incidence in just 2011 and 2016 that included ridership as an additional predictor variable. Our fixed effects were year, socioeconomic status, distance from public transit, a two-way interaction between socioeconomic status and distance from public transit, percent utilizing public transit, and a two-way interaction between socioeconomic status and percent utilizing public transit. As in our model of all years, the model contained a log offset of population per zone per year and dengue case counts were log transformed after adding one to account for zones with zero dengue cases in a given year, year was analyzed as a categorical variable, and all continuous variables were scaled to enable comparison of effect size. The data was truncated to SIT zones that had data for all years available (251 remaining of 291). We used the same model selection process, and again a fixed effect model was chosen, and based on the results of the Lagrange multiplier tests, a Spatial Autoregressive (SAR) model was determined the most appropriate to incorporate spatial dependency. 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

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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

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

Dargie, G. C. et al. Age, extent and carbon storage of the central Congo Basin peatland complex. Nature 542, 86–90 (2017). This study describes the large extent and huge carbon stocks of the Congo Basin peatlands.Article 

Google Scholar 
Page, S. E., Rieley, J. O. & Banks, C. J. Global and regional importance of the tropical peatland carbon pool. Glob. Change Biol. 17, 798–818 (2011). This is a comprehensive assessment of the extent, volume and carbon stocks of peatlands across the tropics, highlighting their importance in the global carbon cycle and key uncertainties.Article 

Google Scholar 
Yu, Z., Loisel, J., Brosseau, D. P., Beilman, D. W. & Hunt, S. J. Global peatland dynamics since the Last Glacial Maximum. Geophys. Res. Lett. 37, L13402 (2010).
Google Scholar 
Gumbricht, T. et al. An expert system model for mapping tropical wetlands and peatlands reveals South America as the largest contributor. Glob. Change Biol. 23, 3581–3599 (2017).Article 

Google Scholar 
Olsson, L. et al. Climate change and land (eds Shukla, P. R. et al.) 345–436 (IPCC, 2019).Leifeld, J. & Menichetti, L. The underappreciated potential of peatlands in global climate change mitigation strategies. Nat. Commun. 9, 1071 (2018).Article 

Google Scholar 
Smith, P. et al. Climate change 2014: mitigation of climate change. Contribution of Working Group III to the fifth assessment report of the Intergovernmental Panel on Climate Change (eds Edenhofer, O. et al.) 811–922 (Cambridge Univ. Press, 2014).Goldstein, A. et al. Protecting irrecoverable carbon in Earth’s ecosystems. Nat. Clim. Chang. 10, 287–295 (2020). This study evaluates ecosystems on the basis of the size of carbon stocks that are vulnerable to release upon land-use conversion and not recoverable on timescales relevant to avoiding dangerous climate impacts; it emphasizes the high density of irrecoverable carbon in tropical peatlands.Article 

Google Scholar 
Griscom, B. W. et al. Natural climate solutions. Proc. Natl. Acad. Sci. USA 114, 11645–11650 (2017).Article 

Google Scholar 
Leifeld, J., Wüst-Galley, C. & Page, S. Intact and managed peatland soils as a source and sink of GHGs from 1850 to 2100. Nat. Clim. Chang. 9, 945–947 (2019).Article 

Google Scholar 
Intergovernmental Panel on Climate Change. Climate change and land (IPCC, 2019).Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).Article 

Google Scholar 
Page, S., Wüst, R. & Banks, C. Past and present carbon accumulation and loss in Southeast Asian peatlands. PAGES News 18, 25–27 (2010).Article 

Google Scholar 
Page, S. E. et al. A record of Late Pleistocene and Holocene carbon accumulation and climate change from an equatorial peat bog (Kalimantan, Indonesia): implications for past, present and future carbon dynamics. J. Quat. Sci. 19, 625–635 (2004).Article 

Google Scholar 
Dommain, R., Couwenberg, J. & Joosten, H. Development and carbon sequestration of tropical peat domes in south-east Asia: links to post-glacial sea-level changes and Holocene climate variability. Quat. Sci. Rev. 30, 999–1010 (2011). This is a comprehensive assessment of peatland development in Southeast Asia, exploring regional differences in rates of peat formation and carbon accumulation.Article 

Google Scholar 
Ruwaimana, M., Anshari, G. Z., Silva, L. C. R. & Gavin, D. G. The oldest extant tropical peatland in the world: a major carbon reservoir for at least 47,000 years. Environ. Res. Lett. 15, 114027 (2020). This study compares the development of coastal and inland peatlands in West Kalimantan, Indonesia, and provides a description of the oldest known peat deposit in Southeast Asia.Article 

Google Scholar 
Anshari, G., Kershaw, A. P., Kaars, S. V. D. & Jacobsen, G. Environmental change and peatland forest dynamics in the Lake Sentarum area, West Kalimantan, Indonesia. J. Quat. Sci. 19, 637–655 (2004).Article 

Google Scholar 
Dommain, R., Couwenberg, J. & Joosten, H. Hydrological self-regulation of domed peatlands in south-east Asia and consequences for conservation and restoration Mires Peat 6, 1–17 2010).
Google Scholar 
Jones, M. B. & Muthuri, F. M. Standing biomass and carbon distribution in a papyrus (Cyperus papyrus L.) swamp on Lake Naivasha, Kenya. J. Trop. Ecol. 13, 347–356 (1997).Article 

Google Scholar 
Saunders, M. J., Jones, M. B. & Kansiime, F. Carbon and water cycles in tropical papyrus wetlands. Wetl. Ecol. Manag. 15, 489–498 (2007).Article 

Google Scholar 
Burrough, S. L., Thomas, D. S. G., Orijemie, E. A. & Willis, K. J. Landscape sensitivity and ecological change in western Zambia: the long-term perspective from dambo cut-and-fill sediments. J. Quat. Sci. 30, 44–58 (2015).Article 

Google Scholar 
Davenport, I. J. et al. First evidence of peat domes in the Congo Basin using LiDAR from a fixed-wing drone. Remote Sens. 12, 2196 (2020).Article 

Google Scholar 
Alsdorf, D. et al. Opportunities for hydrologic research in the Congo Basin. Rev. Geophys. 54, 378–409 (2016).Article 

Google Scholar 
Biddulph, G. E. et al. Current knowledge on the Cuvette Centrale peatland complex and future research directions. Bois For. Trop. 350, 3–14 (2021).Article 

Google Scholar 
Lähteenoja, O. et al. The large Amazonian peatland carbon sink in the subsiding Pastaza–Marañón foreland basin, Peru. Glob. Change Biol. 18, 164–178 (2012).Article 

Google Scholar 
Kelly, T. J. et al. The vegetation history of an Amazonian domed peatland. Palaeogeogr. Palaeoclimatol. Palaeoecol. 468, 129–141 (2017).Article 

Google Scholar 
Draper, F. C. et al. The distribution and amount of carbon in the largest peatland complex in Amazonia. Environ. Res. Lett. 9, 124017 (2014). Using a combination of remote sensing and field data, this study provides an assessment of the distribution of above- and belowground peatland carbon stocks in the Pastaza–Marañon foreland basin in Peruvian Amazonia.Article 

Google Scholar 
Phillips, S., Rouse, G. E. & Bustin, R. M. Vegetation zones and diagnostic pollen profiles of a coastal peat swamp, Bocas del Toro, Panamá. Palaeogeogr. Palaeoclimatol. Palaeoecol. 128, 301–338 (1997).Article 

Google Scholar 
Sjögersten, S. et al. Coastal wetland ecosystems deliver large carbon stocks in tropical Mexico. Geoderma 403, 115173 (2021).Article 

Google Scholar 
Joosten, H. in Tropical Peatland Ecosystems (eds Osaki, M. & Tsuji, N.) 33–48 (Springer, 2016).Anderson, J. A. R. in Mires: Swamp, Bog, Fen and Moor: Regional Studies (ed. Gore, A. J. P.) 191–199 (Elsevier, 1983).Draper, F. C. et al. Peatland forests are the least diverse tree communities documented in Amazonia, but contribute to high regional beta-diversity. Ecography 41, 1256–1269 (2018).Article 

Google Scholar 
Anderson, J. A. R. Ecology and Forest Types of The Peat Swamp Forests of Sarawak and Brunei in Relation to their Silviculture. Thesis, Univ. Edinburgh (1961).Freund, C. A., Harsanto, F. A., Purwanto, A., Takahashi, H. & Harrison, M. E. Microtopographic specialization and flexibility in tropical peat swamp forest tree species. Biotropica 50, 208–214 (2018).Article 

Google Scholar 
Lampela, M. et al. Ground surface microtopography and vegetation patterns in a tropical peat swamp forest. CATENA 139, 127–136 (2016).Article 

Google Scholar 
Miyamoto, K. et al. Habitat differentiation among tree species with small-scale variation of humus depth and topography in a tropical heath forest of Central Kalimantan, Indonesia. J. Trop. Ecol. 19, 43–54 (2003).Article 

Google Scholar 
Miettinen, J., Shi, C. & Liew, S. C. Land cover distribution in the peatlands of peninsular Malaysia, Sumatra and Borneo in 2015 with changes since 1990. Glob. Ecol. Conserv. 6, 67–78 (2016).Article 

Google Scholar 
Wijedasa, L. S. et al. Carbon emissions from South-East Asian peatlands will increase despite emission-reduction schemes. Glob. Change Biol. 24, 4598–4613 (2018).Article 

Google Scholar 
Page, S. E. & Hooijer, A. In the line of fire: the peatlands of Southeast Asia. Phil. Trans. R. Soc. B 371, 20150176.(2016).Article 

Google Scholar 
Hergoualc’h, K., Gutiérrez-Vélez, V. H., Menton, M. & Verchot, L. V. Characterizing degradation of palm swamp peatlands from space and on the ground: an exploratory study in the Peruvian Amazon. For. Ecol. Manag. 393, 63–73 (2017).Article 

Google Scholar 
Horn, C. M., Vargas Paredes, V. H., Gilmore, M. P. & Endress, B. A. Spatio-temporal patterns of Mauritia flexuosa fruit extraction in the Peruvian Amazon: implications for conservation and sustainability. Appl. Geogr. 97, 98–108 (2018).Article 

Google Scholar 
Dargie, G. C. et al. Congo Basin peatlands: threats and conservation priorities. Mitig. Adapt. Strateg. Glob. Change 24, 669–686 (2019).Article 

Google Scholar 
Grundling, P.-L. & Grootjans, A. P. in The Wetland Book. II: Distribution, Description, and Conservation (eds Finlayson, M., Milton, G., Prentice, R. & Davidson, N.) (Springer, 2018).Roucoux, K. H. et al. Threats to intact tropical peatlands and opportunities for their conservation. Conserv. Biol. 31, 1283–1292 (2017).Article 

Google Scholar 
Baird, A. J. et al. High permeability explains the vulnerability of the carbon store in drained tropical peatlands. Geophys. Res. Lett. 44, 1333–1339 (2017). This study finds that the permeability of ombrotrophic tropical peat is higher than expected, resulting in deep water tables in ditched tropical peatlands and associated high rates of peat oxidation.Article 

Google Scholar 
Kelly, T. J. et al. The high hydraulic conductivity of three wooded tropical peat swamps in northeast Peru: measurements and implications for hydrological function. Hydrol. Process. 28, 3373–3387 (2014).Article 

Google Scholar 
Tonks, A. J. et al. Impacts of conversion of tropical peat swamp forest to oil palm plantation on peat organic chemistry, physical properties and carbon stocks. Geoderma 289, 36–45 (2017).Article 

Google Scholar 
Mezbahuddin, M., Grant, R. F. & Hirano, T. How hydrology determines seasonal and interannual variations in water table depth, surface energy exchange, and water stress in a tropical peatland: modeling versus measurements. J. Geophys. Res. Biogeosci. 120, 2132–2157 (2015).Article 

Google Scholar 
Laurén, A. et al. Nutrient balance as a tool for maintaining yield and mitigating environmental impacts of Acacia plantation in drained tropical peatland — description of plantation simulator. Forests 12, 312 (2021).Article 

Google Scholar 
Hooijer, A. et al. Subsidence and carbon loss in drained tropical peatlands. Biogeosciences 9, 1053–1071 (2012).Article 

Google Scholar 
Anshari, G. Z., Gusmayanti, E. & Novita, N. The use of subsidence to estimate carbon loss from deforested and drained tropical peatlands in Indonesia. Forests 12, 732 (2021).Article 

Google Scholar 
Evans, C. D. et al. A novel low-cost, high-resolution camera system for measuring peat subsidence and water table dynamics. Front. Environ. Sci. 9, 33 (2021).
Google Scholar 
Evans, C. D. et al. Rates and spatial variability of peat subsidence in Acacia plantation and forest landscapes in Sumatra, Indonesia. Geoderma 338, 410–421 (2019).Article 

Google Scholar 
Hoyt, A. M., Chaussard, E., Seppalainen, S. S. & Harvey, C. F. Widespread subsidence and carbon emissions across Southeast Asian peatlands. Nat. Geosci. 13, 435–440 (2020). Using remote sensing, this study quantifies the rate of peat subsidence and carbon loss across peatlands in Southeast Asia.Article 

Google Scholar 
Cobb, A. R., Dommain, R., Tan, F., Heng, N. H. E. & Harvey, C. F. Carbon storage capacity of tropical peatlands in natural and artificial drainage networks. Environ. Res. Lett. 15, 114009 (2020).Article 

Google Scholar 
Ritzema, H., Limin, S., Kusin, K., Jauhiainen, J. & Wösten, H. Canal blocking strategies for hydrological restoration of degraded tropical peatlands in central Kalimantan, Indonesia. CATENA 114, 11–20 (2014).Article 

Google Scholar 
Hooijer, A., Vernimmen, R., Visser, M. & Mawdsley, N. Flooding projections from elevation and subsidence models for oil palm plantations in the Rajang Delta peatlands, Sarawak, Malaysia (Deltares, 2015).Sumarga, E., Hein, L., Hooijer, A. & Vernimmen, R. Hydrological and economic effects of oil palm cultivation in Indonesian peatlands. Ecol. Soc. 21, 52 (2016).Article 

Google Scholar 
Evers, S., Yule, C. M., Padfield, R., O’Reilly, P. & Varkkey, H. Keep wetlands wet: the myth of sustainable development of tropical peatlands — implications for policies and management. Glob. Change Biol. 23, 534–549 (2017). This study reviews the ecosystem services provided by Southeast Asian peatlands and discusses key policy challenges for peatland management.Article 

Google Scholar 
Tan, Z. D., Lupascu, M. & Wijedasa, L. S. Paludiculture as a sustainable land use alternative for tropical peatlands: a review. Sci. Total Environ. 753, 142111 (2021). This study evaluates the current understanding of and opportunities for paludiculture in the context of tropical peatlands, emphasizing that tropical paludiculture will be heavily influenced by socioeconomic considerations.Article 

Google Scholar 
Haraguchi, A. in Tropical Peatland Ecosystems (Osaki, M. & Tsuji, N.) 297–311 (Springer, 2016).Wösten, J. H. M., Ismail, A. B. & van Wijk, A. L. M. Peat subsidence and its practical implications: a case study in Malaysia. Geoderma 78, 25–36 (1997).Article 

Google Scholar 
Grealish, G. J. & Fitzpatrick, R. W. Acid sulphate soil characterization in Negara Brunei Darussalam: a case study to inform management decisions. Soil. Use Manag. 29, 432–444 (2013).Article 

Google Scholar 
Klepper, O., Chairuddin, G. T., Iriansyah & Rijksen, H. D. Water quality and the distribution of some fishes in an area of acid sulphate soils, Kalimantan, Indonesia. Hydrobiol. Bull. 25, 217–224 (1992).Article 

Google Scholar 
Shamshuddin, J. & Muhrizal, S. Chemical pollution in acid sulfate soils. Proc. Geol. Soc. Malaysia Annu. Geol.Conf. 2000, 231–234 (2000).
Google Scholar 
Suwardi. Utilization and improvement of marginal soils for agricultural development in Indonesia. IOP Conf. Ser. Earth Environ. Sci. 383, 012047 (2019).Article 

Google Scholar 
Hirano, T., Jauhiainen, J., Inoue, T. & Takahashi, H. Controls on the carbon balance of tropical peatlands. Ecosystems 12, 873–887 (2009).Article 

Google Scholar 
Stumm, W. & Morgan, J. J. Aquatic Chemistry: Chemical Equilibria and Rates in Natural Waters (Wiley, 1996).Billett, M. F., Garnett, M. H. & Dinsmore, K. J. Should aquatic CO evasion be included in contemporary carbon budgets for peatland ecosystems? Ecosystems 18, 471–480 (2015).Article 

Google Scholar 
Chimner, R. A. & Ewel, K. C. A tropical freshwater wetland: II. Production, decomposition, and peat formation. Wetl. Ecol. Manag. 13, 671–684 (2005).Article 

Google Scholar 
Hoyos-Santillan, J. et al. Getting to the root of the problem: litter decomposition and peat formation in lowland neotropical peatlands. Biogeochemistry 126, 115–129 (2015).Article 

Google Scholar 
Könönen, M. et al. Land use increases the recalcitrance of tropical peat. Wetl. Ecol. Manag. 24, 717–731 (2016).Article 

Google Scholar 
Sangok, F. E., Maie, N., Melling, L. & Watanabe, A. Evaluation on the decomposability of tropical forest peat soils after conversion to an oil palm plantation. Sci. Total Environ. 587–588, 381–388 (2017).Article 

Google Scholar 
Yule, C. M., Lim, Y. Y. & Lim, T. Y. Degradation of tropical Malaysian peatlands decreases levels of phenolics in soil and in leaves of Macaranga pruinosa. Front. Earth Sci. 4, 1–9 (2016).Article 

Google Scholar 
Yu, Z. et al. Peatlands and their role in the global carbon cycle. Eos 92, 97–98 (2011).Article 

Google Scholar 
Lähteenoja, O., Ruokolainen, K., Schulman, L. & Oinonen, M. Amazonian peatlands: an ignored C sink and potential source. Glob. Change Biol. 15, 2311–2320 (2009).Article 

Google Scholar 
Garneau, M. et al. Holocene carbon dynamics of boreal and subarctic peatlands from Québec, Canada. Holocene 24, 1043–1053 (2014).Article 

Google Scholar 
Gorham, E. Northern peatlands: role in the carbon cycle and probable responses to climatic warming. Ecol. Appl. 1, 182–195 (1991).Article 

Google Scholar 
Turunen, J., Roulet, N. T., Moore, T. R. & Richard, P. J. H. Nitrogen deposition and increased carbon accumulation in ombrotrophic peatlands in eastern Canada. Glob. Biogeochem. Cycles 18, GB3002 (2004).Article 

Google Scholar 
Yu, Z. C. Northern peatland carbon stocks and dynamics: a review. Biogeosciences 9, 4071–4085 (2012).Article 

Google Scholar 
Poulter, B. et al. in Wetland Carbon And Environmental Management (eds Krauss, K. W., Zhu, Z. & Stagg, C. L.) 1–20 (American Geophysical Union, 2021).Honorio Coronado, E. et al. Intensive field sampling increases the known extent of carbon-rich Amazonian peatland pole forests. Environ. Res. Lett. 16, 074048 (2021).Article 

Google Scholar 
Sjögersten, S. et al. Tropical wetlands: a missing link in the global carbon cycle? Carbon cycling in tropical wetlands. Glob. Biogeochem. Cycles 28, 1371–1386 (2014).Article 

Google Scholar 
Griffis, T. J. et al. Hydrometeorological sensitivities of net ecosystem carbon dioxide and methane exchange of an Amazonian palm swamp peatland. Agric. For. Meteorol. 295, 108167 (2020).Article 

Google Scholar 
Kiew, F. et al. CO2 balance of a secondary tropical peat swamp forest in Sarawak, Malaysia. Agric. For. Meteorol. 248, 494–501 (2018).Article 

Google Scholar 
Hirano, T. et al. Effects of disturbances on the carbon balance of tropical peat swamp forests. Glob. Change Biol. 18, 3410–3422 (2012).Article 

Google Scholar 
Tang, A. C. I. et al. A Bornean peat swamp forest is a net source of carbon dioxide to the atmosphere. Glob. Change Biol. 26, 6931–6944 (2020).Article 

Google Scholar 
Deshmukh, C. S. et al. Conservation slows down emission increase from a tropical peatland in Indonesia. Nat. Geosci. 14, 484–490 (2021). This study presented measurements of CO2 and CH4 fluxes obtained using the eddy covariance method from both intact and degraded peat swamp forest in Sumatra, Indonesia, during the 2019 ENSO drought.Article 

Google Scholar 
Kiew, F. et al. Carbon dioxide balance of an oil palm plantation established on tropical peat. Agric. For. Meteorol. 295, 108189 (2020).Article 

Google Scholar 
McCalmont, J. et al. Short- and long-term carbon emissions from oil palm plantations converted from logged tropical peat swamp forest. Glob. Change Biol. 27, 2361–2376 (2021).Article 

Google Scholar 
Germer, J. & Sauerborn, J. Estimation of the impact of oil palm plantation establishment on greenhouse gas balance. Environ. Dev. Sustain. 10, 697–716 (2008).Article 

Google Scholar 
Lewis, K. et al. An assessment of oil palm plantation aboveground biomass stocks on tropical peat using destructive and non-destructive methods. Sci. Rep. 10, 2230 (2020).Article 

Google Scholar 
Wijedasa, L. S. Peat Swamp Forest Conservation in Southeast Asia. Thesis, National Univ. Singapore (2019).Moore, S. et al. Deep instability of deforested tropical peatlands revealed by fluvial organic carbon fluxes. Nature 493, 660–663 (2013).Article 

Google Scholar 
Cook, S. et al. Fluvial organic carbon fluxes from oil palm plantations on tropical peatland. Biogeosciences 15, 7435–7450 (2018).Article 

Google Scholar 
Waldron, S. et al. C mobilisation in disturbed tropical peat swamps: old DOC can fuel the fluvial efflux of old carbon dioxide, but site recovery can occur. Sci. Rep. 9, 11429 (2019).Article 

Google Scholar 
Brady, M. A. Organic Matter Dynamics of Coastal Peat Deposits in Sumatra, Indonesia. Thesis, Univ. British Columbia (1997).Jauhiainen, J., Limin, S., Silvennoinen, H. & Vasander, H. Carbon dioxide and methane fluxes in drained tropical peat before and after hhydrological restoration. Ecology 89, 3503–3514 (2008).Article 

Google Scholar 
Jauhiainen, J., Takahashi, H., Heikkinen, J. E. P., Martikainen, P. J. & Vasander, H. Carbon fluxes from a tropical peat swamp forest floor. Glob. Change Biol. 11, 1788–1797 (2005).Article 

Google Scholar 
Yule, C. M. & Gomez, L. N. Leaf litter decomposition in a tropical peat swamp forest in peninsular Malaysia. Wetl. Ecol. Manag. 17, 231–241 (2009).Article 

Google Scholar 
Swails, E., Hertanti, D., Hergoualc’h, K., Verchot, L. & Lawrence, D. The response of soil respiration to climatic drivers in undrained forest and drained oil palm plantations in an Indonesian peatland. Biogeochemistry 142, 37–51 (2019).Article 

Google Scholar 
Ishikura, K. et al. Carbon dioxide and methane emissions from peat soil in an undrained tropical peat swamp forest. Ecosystems 22, 1852–1868 (2019).Article 

Google Scholar 
Melling, L., Tan, C. Y., Goh, K. J. & Hatano, R. Soil microbial and root respirations from three ecosystems in tropical peatland of Sarawak, Malaysia. J. Oil Palm. Res. 25, 44–57 (2013).
Google Scholar 
Cooper, H. V. et al. Greenhouse gas emissions resulting from conversion of peat swamp forest to oil palm plantation. Nat. Commun. 11, 407 (2020).Article 

Google Scholar 
Girkin, N. T., Turner, B. L., Ostle, N. & Sjögersten, S. Root-derived CO2 flux from a tropical peatland. Wetl. Ecol. Manag. 26, 985–991 (2018).Article 

Google Scholar 
Dhandapani, S., Ritz, K., Evers, S., Yule, C. M. & Sjögersten, S. Are secondary forests second-rate? Comparing peatland greenhouse gas emissions, chemical and microbial community properties between primary and secondary forests in peninsular Malaysia. Sci. Total Environ. 655, 220–231 (2019).Article 

Google Scholar 
Dhandapani, S. et al. Land-use changes associated with oil palm plantations impact PLFA microbial phenotypic community structure throughout the depth of tropical peats. Wetlands 40, 2351–2366 (2020).Article 

Google Scholar 
Mishra, S. et al. Microbial and metabolic profiling reveal strong influence of water table and land-use patterns on classification of degraded tropical peatlands. Biogeosciences 11, 1727–1741 (2014).Article 

Google Scholar 
Mishra, S. et al. Degradation of Southeast Asian tropical peatlands and integrated strategies for their better management and restoration. J. Appl. Ecol. 58, 1370–1387 (2021). This paper reviews current understanding of intact and degraded peatlands in Southeast Asia and proposes an approach for peatland management and restoration involving explicit consideration of interacting ecological factors and the involvement of local communities.Article 

Google Scholar 
Carlson, K. M., Goodman, L. K. & May-Tobin, C. C. Modeling relationships between water table depth and peat soil carbon loss in Southeast Asian plantations. Environ. Res. Lett. 10, 074006 (2015).Article 

Google Scholar 
Carlson, K. M. et al. Committed carbon emissions, deforestation, and community land conversion from oil palm plantation expansion in West Kalimantan, Indonesia. Proc. Natl Acad. Sci. USA 109, 7559–7564 (2012).Article 

Google Scholar 
Couwenberg, J., Dommain, R. & Joosten, H. Greenhouse gas fluxes from tropical peatlands in south-east Asia. Glob. Change Biol. 16, 1715–1732 (2010).Article 

Google Scholar 
Evans, C. D. et al. Overriding water table control on managed peatland greenhouse gas emissions. Nature 593, 548–552 (2021). Using data for CO2 and CH4 fluxes from all major peatland biomes, this paper demonstrates that greenhouse gas emissions from drained agricultural peatlands could be greatly reduced by raising water levels closer to the peat surface while maintaining productive agricultural use.
Google Scholar 
Hooijer, A. et al. Current and future CO2 emissions from drained peatlands in Southeast Asia. Biogeosciences 7, 1505–1514 (2010).Article 

Google Scholar 
Hiraishi, T. et al. (eds) 2013 Supplement to the 2006 IPCC guidelines for national greenhouse gas inventories: wetlands (IPCC, 2014).Jauhiainen, J., Kerojoki, O., Silvennoinen, H., Limin, S. & Vasander, H. Heterotrophic respiration in drained tropical peat is greatly affected by temperature — a passive ecosystem cooling experiment. Environ. Res. Lett. 9, 105013 (2014).Article 

Google Scholar 
Manning, F. C., Kho, L. K., Hill, T. C., Cornulier, T. & Teh, Y. A. Carbon emissions from oil palm plantations on peat soil. Front. For. Glob. Change https://doi.org/10.3389/ffgc.2019.00037 (2019).Article 

Google Scholar 
Deshmukh, C. S. et al. Impact of forest plantation on methane emissions from tropical peatland. Glob. Change Biol. 26, 2477–2495 (2020).Article 

Google Scholar 
Wong, G. X. et al. How do land use practices affect methane emissions from tropical peat ecosystems? Agric. For. Meteorol. 282–283, 107869 (2020).Article 

Google Scholar 
Pangala, S. R. et al. Large emissions from floodplain trees close the Amazon methane budget. Nature 552, 230–234 (2017).Article 

Google Scholar 
Pangala, S. R., Moore, S., Hornibrook, E. R. C. & Gauci, V. Trees are major conduits for methane egress from tropical forested wetlands. N. Phytol. 197, 524–531 (2013).Article 

Google Scholar 
Hergoualc’h, K. et al. Spatial and temporal variability of soil N2O and CH4 fluxes along a degradation gradient in a palm swamp peat forest in the Peruvian Amazon. Glob. Change Biol. 26, 7198–7216 (2020).Article 

Google Scholar 
Teh, Y. A., Murphy, W. A., Berrio, J.-C., Boom, A. & Page, S. E. Seasonal variability in methane and nitrous oxide fluxes from tropical peatlands in the western Amazon basin. Biogeosciences 14, 3669–3683 (2017).Article 

Google Scholar 
Hoyos-Santillan, J. et al. Evaluation of vegetation communities, water table, and peat composition as drivers of greenhouse gas emissions in lowland tropical peatlands. Sci. Total Environ. 688, 1193–1204 (2019).Article 

Google Scholar 
van Haren, J. et al. A versatile gas flux chamber reveals high tree stem CH4 emissions in Amazonian peatland. Agric. For. Meteorol. 307, 108504 (2021).Article 

Google Scholar 
Sjögersten, S. et al. Temperature response of ex-situ greenhouse gas emissions from tropical peatlands: interactions between forest type and peat moisture conditions. Geoderma 324, 47–55 (2018).Article 

Google Scholar 
Girkin, N. T. et al. Spatial variability of organic matter properties determines methane fluxes in a tropical forested peatland. Biogeochemistry 142, 231–245 (2019).Article 

Google Scholar 
Girkin, N. T., Turner, B. L., Ostle, N. & Sjögersten, S. Composition and concentration of root exudate analogues regulate greenhouse gas fluxes from tropical peat. Soil. Biol. Biochem. 127, 280–285 (2018).Article 

Google Scholar 
Girkin, N. T., Vane, C. H., Turner, B. L., Ostle, N. J. & Sjögersten, S. Root oxygen mitigates methane fluxes in tropical peatlands. Environ. Res. Lett. 15, 064013 (2020).Article 

Google Scholar 
Jauhiainen, J., Silvennoinen, H., Könönen, M., Limin, S. & Vasander, H. Management driven changes in carbon mineralization dynamics of tropical peat. Biogeochemistry 129, 115–132 (2016).Article 

Google Scholar 
Wright, E. L. et al. Contribution of subsurface peat to CO2 and CH fluxes in a neotropical peatland. Glob. Change Biol. 17, 2867–2881 (2011).Article 

Google Scholar 
Prananto, J. A., Minasny, B., Comeau, L., Rudiyanto, R. & Grace, P. Drainage increases CO2 and N2O emissions from tropical peat soils. Glob. Change Biol. 26, 4583–4600 (2020).Article 

Google Scholar 
Peacock, M. et al. Global importance of methane emissions from drainage ditches and canals. Environ. Res. Lett. 16, 044010 (2021).Article 

Google Scholar 
Chuang, P.-C. et al. Methane fluxes from tropical coastal lagoons surrounded by mangroves, Yucatán, Mexico. J. Geophys. Res. Biogeosci. 122, 1156–1174 (2017).Article 

Google Scholar 
Jauhiainen, J. & Silvennoinen, H. Diffusion GHG fluxes at tropical peatland drainage canal water surfaces. Suoseura 63, 93–105 (2012).
Google Scholar 
Yupi, H. M., Inoue, T. & Bathgate, J. Concentrations, loads and yields of organic carbon from two tropical peat swamp forest streams in Riau Province, Sumatra, Indonesia. Mires Peat 18, 1–15 (2016).
Google Scholar 
Zhou, Y., Evans, C. D., Chen, Y., Chang, K. Y. W. & Martin, P. Extensive remineralization of peatland-derived dissolved organic carbon and ocean acidification in the Sunda Shelf Sea, Southeast Asia. J. Geophys. Res. Ocean. 126, e2021JC017292 (2021).
Google Scholar 
Alkhatib, M., Jennerjahn, T. C. & Samiaji, J. Biogeochemistry of the Dumai River estuary, Sumatra, Indonesia, a tropical black-water river. Limnol. Oceanogr. 52, 2410–2417 (2007).Article 

Google Scholar 
Gandois, L. et al. From canals to the coast: dissolved organic matter and trace metal composition in rivers draining degraded tropical peatlands in Indonesia. Biogeosciences 17, 1897–1909 (2020).Article 

Google Scholar 
Rixen, T. et al. The Siak, a tropical black water river in central Sumatra on the verge of anoxia. Biogeochemistry 90, 129–140 (2008).Article 

Google Scholar 
Miettinen, J., Hooijer, A., Vernimmen, R., Liew, S. C. & Page, S. E. From carbon sink to carbon source: extensive peat oxidation in insular Southeast Asia since 1990. Environ. Res. Lett. 12, 024014 (2017).Article 

Google Scholar 
Loisel, J. et al. Expert assessment of future vulnerability of the global peatland carbon sink. Nat. Clim. Chang. 11, 70–77 (2021).Article 

Google Scholar 
Boysen, L. R. et al. Global and regional effects of land-use change on climate in 21st century simulations with interactive carbon cycle. Earth Syst. Dyn. 5, 309–319 (2014).Article 

Google Scholar 
Sitch, S. et al. Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences 12, 653–679 (2015).Article 

Google Scholar 
Mitchard, E. T. A. The tropical forest carbon cycle and climate change. Nature 559, 527–534 (2018).Article 

Google Scholar 
Naidu, D. G. T. & Bagchi, S. Greening of the Earth does not compensate for rising soil heterotrophic respiration under climate change. Glob. Change Biol. 27, 2029–2038 (2021).Article 

Google Scholar 
Li, W. et al. Future precipitation changes and their implications for tropical peatlands. Geophys. Res. Lett. 34, 01403 (2007).Article 

Google Scholar 
Barichivich, J. et al. Recent intensification of Amazon flooding extremes driven by strengthened Walker circulation. Sci. Adv. 4, eaat8785 (2018).Article 

Google Scholar 
Marengo, J. A. et al. Changes in climate and land use over the Amazon region: current and future variability and trends. Front. Earth Sci. 6, 228 (2018).Article 

Google Scholar 
Cobb, A. R. et al. How temporal patterns in rainfall determine the geomorphology and carbon fluxes of tropical peatlands. Proc. Natl. Acad. Sci. USA 114, E5187–E5196 (2017).Article 

Google Scholar 
Cai, W. et al. Increased variability of eastern Pacific El Niño under greenhouse warming. Nature 564, 201–206 (2018).Article 

Google Scholar 
Rifai, S. W., Li, S. & Malhi, Y. Coupling of El Niño events and long-term warming leads to pervasive climate extremes in the terrestrial tropics. Environ. Res. Lett. 14, 105002 (2019).Article 

Google Scholar 
Girkin, N. T. et al. Interactions between labile carbon, temperature and land use regulate carbon dioxide and methane production in tropical peat. Biogeochemistry 147, 87–97 (2020).Article 

Google Scholar 
Cole, L. E. S., Bhagwat, S. A. & Willis, K. J. Long-term disturbance dynamics and resilience of tropical peat swamp forests. J. Ecol. 103, 16–30 (2015).Article 

Google Scholar 
Weiss, D. et al. The geochemistry of major and selected trace elements in a forested peat bog, Kalimantan, SE Asia, and its implications for past atmospheric dust deposition. Geochim. Cosmochim. Acta 66, 2307–2323 (2002).Article 

Google Scholar 
Lähteenoja, O. & Page, S. High diversity of tropical peatland ecosystem types in the Pastaza-Marañón basin, Peruvian Amazonia. J. Geophys. Res. 116, G02025 (2011).
Google Scholar 
Roucoux, K. H. et al. Vegetation development in an Amazonian peatland. Palaeogeogr. Palaeoclimatol. Palaeoecol. 374, 242–255 (2013).Article 

Google Scholar 
Lampela, M., Jauhiainen, J. & Vasander, H. Surface peat structure and chemistry in a tropical peat swamp forest. Plant. Soil. 382, 329–347 (2014).Article 

Google Scholar 
Page, S. E., Rieley, J. O., Shotyk, Ø. W. & Weiss, D. Interdependence of peat and vegetation in a tropical peat swamp forest. Phil. Trans. R. Soc. Lond. B 354, 1885–1897 (1999).Article 

Google Scholar 
Sjögersten, S., Cheesman, A. W., Lopez, O. & Turner, B. L. Biogeochemical processes along a nutrient gradient in a tropical ombrotrophic peatland. Biogeochemistry 104, 147–163 (2011).Article 

Google Scholar 
Yule, C. M. Loss of biodiversity and ecosystem functioning in Indo-Malayan peat swamp forests. Biodivers. Conserv. 19, 393–409 (2010).Article 

Google Scholar 
Basilier, K. Moss-associated nitrogen fixation in some mire and coniferous forest environments around Uppsala, Sweden. Lindbergia 5, 84–88 (1979).
Google Scholar 
Ong, C. S. P., Juan, J. C. & Yule, C. M. Litterfall production and chemistry of Koompassia malaccensis and Shorea uliginosa in a tropical peat swamp forest: plant nutrient regulation and climate relationships. Trees 29, 527–537 (2015).Article 

Google Scholar 
Wüst, R. A. J. & Bustin, R. M. Opaline and Al–Si phytoliths from a tropical mire system of West Malaysia: abundance, habit, elemental composition, preservation and significance. Chem. Geol. 200, 267–292 (2003).Article 

Google Scholar 
Neuzil, S. G., Cecil, C. B., Kane, J. S. & Soedjono, K. in Modern and Ancient Coal-Forming Environments Vol. 286 (Geological Society of America, 1993).Too, C. C., Keller, A., Sickel, W., Lee, S. M. & Yule, C. M. Microbial community structure in a Malaysian tropical peat swamp forest: the influence of tree species and depth. Front. Microbiol. 9, 2859 (2018).Article 

Google Scholar 
Sulistiyanto, Y. Nutrient Dynamics in Different Sub-types of Peat Swamp Forest in Central Kalimantan, Indonesia. Thesis, Univ. Nottingham (2005).Hoyos Santillán, J. Controls of Carbon Turnover in Tropical Peatlands. Thesis, Univ. Nottingham (2014).Damman, A. W. H. Distribution and movement of elements in ombrotrophic peat bogs. Oikos 30, 480–495 (1978).Article 

Google Scholar 
Laiho, R. & Laine, J. Nitrogen and phosphorus stores in peatlands drained for forestry in Finland. Scand. J. For. Res. 9, 251–260 (1994).Article 

Google Scholar 
Wang, M., Moore, T. R., Talbot, J. & Riley, J. L. The stoichiometry of carbon and nutrients in peat formation. Glob. Biogeochem. Cycles 29, 113–121 (2015).Article 

Google Scholar 
Hodgkins, S. B. et al. Tropical peatland carbon storage linked to global latitudinal trends in peat recalcitrance. Nat. Commun. 9, 3640 (2018).Article 

Google Scholar 
Jackson, C. R., Liew, K. C. & Yule, C. M. Structural and functional changes with depth in microbial communities in a tropical Malaysian peat swamp forest. Microb. Ecol. 57, 402–412 (2009).Article 

Google Scholar 
Kolb, S. & Horn, M. A. Microbial CH4 and NO consumption in acidic wetlands. Front. Microbiol. 3, 78 (2012).Article 

Google Scholar 
Golovchenko, A. V., Tikhonova, E. Y. & Zvyagintsev, D. G. Abundance, biomass, structure, and activity of the microbial complexes of minerotrophic and ombrotrophic peatlands. Microbiology 76, 630–637 (2007).Article 

Google Scholar 
Martikainen, P. J., Nykänen, H., Crill, P. & Silvola, J. Effect of a lowered water table on nitrous oxide fluxes from northern peatlands. Nature 366, 51–53 (1993).Article 

Google Scholar 
Davidson, E. A., Keller, M., Erickson, H. E., Verchot, L. V. & Veldkamp, E. Testing a conceptual model of soil emissions of nitrous and nitric oxides. Bioscience 50, 667 (2000).Article 

Google Scholar 
Rubol, S., Silver, W. L. & Bellin, A. Hydrologic control on redox and nitrogen dynamics in a peatland soil. Sci. Total Environ. 432, 37–46 (2012).Article 

Google Scholar 
Jauhiainen, J. et al. Nitrous oxide fluxes from tropical peat with different disturbance history and management. Biogeosciences 9, 1337–1350 (2012).Article 

Google Scholar 
Könönen, M., Jauhiainen, J., Laiho, R., Kusin, K. & Vasander, H. Physical and chemical properties of tropical peat under stabilised land uses. Mires Peat 16, 1–13 (2015).
Google Scholar 
Chotimah, H., Jaya, A., Suparto, H., Saraswati, D. & Nawansyah, W. Utilizing organic fertilizers on two types of soil to improve growth and yield of Bawang Dayak (Eleutherine americana Merr). Agrivita J. Agric. Sci. 43, 164–173 (2021).
Google Scholar 
Mohidin, H. et al. Optimum levels of N, P, and K nutrition for oil palm seedlings grown in tropical peat soil. J. Plant. Nutr. 42, 1461–1471 (2019).Article 

Google Scholar 
Mutert, E., Fairhurst, T. H. & Von Uexküll, H. R. Agronomic management of oil palms on deep peat. Better. Crop. Int. 13, 22–27 (1999).
Google Scholar 
Hashim, S. A., Teh, C. B. S. & Ahmed, O. H. Influence of water table depths, nutrients leaching losses, subsidence of tropical peat soil and oil palm (Elaeis guineensis Jacq.) seedling growth. Malays. J. Soil. Sci. 23, 13–30 (2019).
Google Scholar 
Oktarita, S., Hergoualc’h, K., Anwar, S. & Verchot, L. V. Substantial N2O emissions from peat decomposition and N fertilization in an oil palm plantation exacerbated by hotspots. Environ. Res. Lett. 12, 104007 (2017).Article 

Google Scholar 
Hoyos-Santillan, J. et al. Root oxygen loss from Raphia taedigera palms mediates greenhouse gas emissions in lowland neotropical peatlands. Plant. Soil. 404, 47–60 (2016).Article 

Google Scholar 
Hatano, R. Impact of land use change on greenhouse gases emissions in peatland: a review. Int. Agrophys. 33, 167–173 (2019). This study reviews the impacts of changes in water-table level and nitrogen inputs on greenhouse gas emissions in tropical and northern peatlands and evaluates the optimal water-table level for minimizing emissions.Article 

Google Scholar 
Zawawi, N. Z. et al. The effect of nitrogen fertiliser on nitrous oxide emission in oil palm plantation. Proc. 15th Int. Peat Congress 355, 515–518 (2016).
Google Scholar 
Turetsky, M. R. et al. Global vulnerability of peatlands to fire and carbon loss. Nat. Geosci. 8, 11–14 (2015). This paper reviews peatland vulnerability to burning, fire-driven carbon emissions and current and future risks of peatland fires.Article 

Google Scholar 
Hu, Y. et al. Review of emissions from smouldering peat fires and their contribution to regional haze episodes. Int. J. Wildland Fire 27, 293–312 (2018).Article 

Google Scholar 
Huijnen, V. et al. Fire carbon emissions over maritime southeast Asia in 2015 largest since 1997. Sci. Rep. 6, 26886 (2016).Article 

Google Scholar 
Smith, T. E. L., Evers, S., Yule, C. M. & Gan, J. Y. In situ tropical peatland fire emission factors and their variability, as determined by field measurements in peninsula Malaysia. Glob. Biogeochem. Cycles 32, 18–31 (2018).Article 

Google Scholar 
Stockwell, C. E. et al. Field measurements of trace gases and aerosols emitted by peat fires in Central Kalimantan, Indonesia, during the 2015 El Niño. Atmos. Chem. Phys. 16, 11711–11732 (2016).Article 

Google Scholar 
Betha, R. et al. Chemical speciation of trace metals emitted from Indonesian peat fires for health risk assessment. Atmos. Res. 122, 571–578 (2013).Article 

Google Scholar 
Breulmann, G. et al. Heavy metals in emergent trees and pioneers from tropical forest with special reference to forest fires and local pollution sources in Sarawak, Malaysia. Sci. Total Environ. 285, 107–115 (2002).Article 

Google Scholar 
Othman, M. & Latif, M. T. Dust and gas emissions from small-scale peat combustion. Aerosol Air Qual. Res. 13, 1045–1059 (2013).Article 

Google Scholar 
See, S. W., Balasubramanian, R. & Wang, W. A study of the physical, chemical, and optical properties of ambient aerosol particles in Southeast Asia during hazy and nonhazy days. J. Geophys. Res. 111, D10S08 (2006).
Google Scholar 
Nikonovas, T., Spessa, A., Doerr, S. H., Clay, G. D. & Mezbahuddin, S. Near-complete loss of fire-resistant primary tropical forest cover in Sumatra and Kalimantan. Commun. Earth Env. 1, 65 (2020).Article 

Google Scholar 
Field, R. D., van der Werf, G. R. & Shen, S. S. P. Human amplification of drought-induced biomass burning in Indonesia since 1960. Nat. Geosci. 2, 185–188 (2009).Article 

Google Scholar 
Astiani, D., Taherzadeh, M. J., Gusmayanti, E., Widiastuti, T. & Burhanuddin, B. Local knowledge on landscape sustainable-hydrological management reduces soil CO2 emission, fire risk and biomass loss in west Kalimantan peatland, Indonesia. Biodiversiitas J. Biol. Divers. 20, 725–731 (2019).Article 

Google Scholar 
Cattau, M. E. et al. Sources of anthropogenic fire ignitions on the peat-swamp landscape in Kalimantan, Indonesia. Glob. Environ. Change 39, 205–219 (2016).Article 

Google Scholar 
Edwards, R. B., Naylor, R. L., Higgins, M. M. & Falcon, W. P. Causes of Indonesia’s forest fires. World Dev. 127, 104717 (2020).Article 

Google Scholar 
Field, R. D. & Shen, S. S. P. Predictability of carbon emissions from biomass burning in Indonesia from 1997 to 2006. J. Geophys. Res. Biogeosci. 113, G04024 (2008).Article 

Google Scholar 
Sloan, S., Locatelli, B., Wooster, M. J. & Gaveau, D. L. A. Fire activity in Borneo driven by industrial land conversion and drought during El Niño periods, 1982–2010. Glob. Environ. Change 47, 95–109 (2017).Article 

Google Scholar 
Page, S. E. et al. The amount of carbon released from peat and forest fires in Indonesia during 1997. Nature 420, 61–65 (2002).Article 

Google Scholar 
World Bank. The cost of fire: an economic analysis of Indonesia’s 2015 fire crisis (World Bank, 2016).Tacconi, L. Preventing fires and haze in Southeast Asia. Nat. Clim. Chang. 6, 640–643 (2016).Article 

Google Scholar 
Lupascu, M., Akhtar, H., Smith, T. E. L. & Sukri, R. S. Post-fire carbon dynamics in the tropical peat swamp forests of Brunei reveal long-term elevated CH4 flux. Glob. Change Biol. 26, 5125–5145 (2020).Article 

Google Scholar 
Milner, L. E. Influence of Fire on Peat Organic Matter from Indonesian Tropical Peatlands. Thesis, Univ. Leicester (2013).Saharjo, B. H. & Nurhayati, A. D. Changes in chemical and physical properties of hemic peat under fire-based shifting cultivation. Tropics 14, 263–269 (2005).Article 

Google Scholar 
Dhandapani, S. & Evers, S. Oil palm ‘slash-and-burn’ practice increases post-fire greenhouse gas emissions and nutrient concentrations in burnt regions of an agricultural tropical peatland. Sci. Total Environ. 742, 140648 (2020).Article 

Google Scholar 
Konecny, K. et al. Variable carbon losses from recurrent fires in drained tropical peatlands. Glob. Change Biol. 22, 1469–1480 (2016).Article 

Google Scholar 
Akhtar, H. et al. Significant sedge-mediated methane emissions from degraded tropical peatlands. Environ. Res. Lett. 16, 014002 (2020).
Google Scholar 
Rein, G. in Fire Phenomena and the Earth System (ed. Belcher, C. M.) 15–33 (Wiley, 2013).Graham, L. L. B. & Page, S. E. A limited seed bank in both natural and degraded tropical peat swamp forest: the implications for restoration. Mires Peat 22, 02 (2018).
Google Scholar 
Graham, E. B. et al. Microbes as engines of ecosystem function: when does community structure enhance predictions of ecosystem processes? Front. Microbiol. 7, 214 (2016).
Google Scholar 
Page, S. et al. Restoration ecology of lowland tropical peatlands in Southeast Asia: current knowledge and future research directions. Ecosystems 12, 888–905 (2009).Article 

Google Scholar 
Sazawa, K. et al. Impact of peat fire on the soil and export of dissolved organic carbon in tropical peat soil, Central Kalimantan, Indonesia. ACS Earth Space Chem. 2, 692–701 (2018).Article 

Google Scholar 
Dove, N. C. & Hart, S. C. Fire reduces fungal species richness and in situ mycorrhizal colonization: a meta-analysis. Fire Ecol. 13, 37–65 (2017).Article 

Google Scholar 
Veldkamp, E., Schmidt, M., Powers, J. S. & Corre, M. D. Deforestation and reforestation impacts on soils in the tropics. Nat. Rev. Earth Env. 1, 590–605 (2020).Article 

Google Scholar 
Qie, L. et al. Long-term carbon sink in Borneo’s forests halted by drought and vulnerable to edge effects. Nat. Commun. 8, 1966 (2017).Article 

Google Scholar 
Giesen, W. & Sari, E. N. N. Tropical peatland restoration report: the Indonesian case. MCA Indonesia https://doi.org/10.13140/RG.2.2.30049.40808 (2018).Article 

Google Scholar 
Dohong, A., Abdul Aziz, A. & Dargusch, P. A review of techniques for effective tropical peatland restoration. Wetlands 38, 275–292 (2018).Article 

Google Scholar 
Shell. Redd+ Katingan Mentaya, Indonesia. Shell https://www.shell.co.uk/motorist/make-the-change-drive-carbon-neutral/redd-plus-katingan-mentaya-indonesia.html (2021).Uda, S. K., Hein, L. & Sumarga, E. Towards sustainable management of Indonesian tropical peatlands. Wetl. Ecol. Manag. 25, 683–701 (2017).Article 

Google Scholar 
Wichtmann, W., Tanneberger, F., Wichmann, S. & Joosten, H. Paludiculture is paludifuture: climate, biodiversity and economic benefits from agriculture and forestry on rewetted peatland. Peatl. Int. 1, 48–51 (2010).
Google Scholar 
Giesen, W. in Tropical Peatland Eco-Management (eds Osaki, M., Tsuji, N., Foead, N. & Rieley, J.) 411–441 (Springer, 2021).Shurpali, N. J. et al. Atmospheric impact of bioenergy based on perennial crop (reed canary grass, Phalaris arundinaceae, L.) cultivation on a drained boreal organic soil. GCB Bioenergy 2, 130–138 (2010).
Google Scholar 
Lawson, I. T. et al. Improving estimates of tropical peatland area, carbon storage, and greenhouse gas fluxes. Wetl. Ecol. Manag. 23, 327–346 (2015).Article 

Google Scholar 
Anda, M. et al. Revisiting tropical peatlands in Indonesia: semi-detailed mapping, extent and depth distribution assessment. Geoderma 402, 115235 (2021).Article 

Google Scholar 
Saxon, E. C., Neuzil, S. G., Biladi, D. B. C., Kinser, J. & Sheppard, S. M. 3D mapping of lowland coastal peat domes in Indonesia. Mires Peat 27, 1–18 (2021).
Google Scholar 
Silvestri, S. et al. Quantification of peat thickness and stored carbon at the landscape scale in tropical peatlands: a comparison of airborne geophysics and an empirical topographic method. J. Geophys. Res. Earth Surf. 124, 3107–3123 (2019).Article 

Google Scholar 
Vernimmen, R. et al. Mapping deep peat carbon stock from a LiDAR based DTM and field measurements, with application to eastern Sumatra. Carbon Balance Manag. 15, 4 (2020).Article 

Google Scholar 
Andersen, R., Chapman, S. J. & Artz, R. R. E. Microbial communities in natural and disturbed peatlands: a review. Soil. Biol. Biochem. 57, 979–994 (2013).Article 

Google Scholar 
Morrison, E. S. et al. Characterization of bacterial and fungal communities reveals novel consortia in tropical oligotrophic peatlands. Microb. Ecol. 82, 188–201 (2020).Article 

Google Scholar 
Finn, D. R. et al. Methanogens and methanotrophs show nutrient-dependent community assemblage patterns across tropical peatlands of the Pastaza–Marañón Basin, Peruvian Amazonia. Front. Microbiol. 11, 746 (2020).Article 

Google Scholar 
Troxler, T. G. et al. Patterns of soil bacteria and canopy community structure related to tropical peatland development. Wetlands 32, 769–782 (2012).Article 

Google Scholar 
Tripathi, B. M. et al. Distinctive tropical forest variants have unique soil microbial communities, but not always low microbial diversity. Front. Microbiol. 7, 376 (2016).Article 

Google Scholar 
Kwon, M. J., Haraguchi, A. & Kang, H. Long-term water regime differentiates changes in decomposition and microbial properties in tropical peat soils exposed to the short-term drought. Soil. Biol. Biochem. 60, 33–44 (2013).Article 

Google Scholar 
Hadi, A. et al. Effects of land-use change in tropical peat soil on the microbial population and emission of greenhouse gases. Microbes Env. 16, 79–86 (2001).Article 

Google Scholar 
Kusai, N. A., Ayob, Z., Maidin, M. S. T., Safari, S. & Ahmad Ali, S. R. Characterization of fungi from different ecosystems of tropical peat in Sarawak, Malaysia. Rendiconti Lincei Sci. Fis. E 29, 469–482 (2018).Article 

Google Scholar 
Shuhada, S. N., Salim, S., Nobilly, F., Zubaid, A. & Azhar, B. Logged peat swamp forest supports greater macrofungal biodiversity than large-scale oil palm plantations and smallholdings. Ecol. Evol. 7, 7187–7200 (2017).Article 

Google Scholar 
Liu, B. et al. The microbial diversity and structure in peatland forest in Indonesia. Soil. Use Manag. 36, 123–138 (2020).Article 

Google Scholar 
Moyersoen, B., Becker, P. & Alexander, I. J. Are ectomycorrhizas more abundant than arbuscular mycorrhizas in tropical heath forests? N. Phytol. 150, 591–599 (2001).Article 

Google Scholar 
Muliyani, R. B., Sastrahidayat, I. R., Abdai, A. L. & Djauhari, S. Exploring ectomycorrhiza in peat swamp forest of Nyaru Menteng Palangka Raya Central Borneo. J. Biodivers. Environ. Sci. 5, 133–145 (2014).
Google Scholar 
Turjaman, M. et al. Improvement of early growth of two tropical peat-swamp forest tree species Ploiarium alternifolium and Calophyllum hosei by two arbuscular mycorrhizal fungi under greenhouse conditions. New Forests 36, 1–12 (2008).Article 

Google Scholar 
Tawaraya, K. et al. Arbuscular mycorrhizal colonization of tree species grown in peat swamp forests of Central Kalimantan, Indonesia. For. Ecol. Manag. 182, 381–386 (2003).Article 

Google Scholar 
Fenner, N. & Freeman, C. Drought-induced carbon loss in peatlands. Nat. Geosci. 4, 895–900 (2011).Article 

Google Scholar 
Yuwati, T. W. & Putri, W. S. Diversity of arbuscular mycorrhiza spores under Shorea balangeran (Korth.) Burck. plantation as bioindicator for the revegetation success. J. Galam 1, 15–26 (2020).Article 

Google Scholar 
Graham, L. L. B., Turjaman, M. & Page, S. E. Shorea balangeran and Dyera polyphylla (syn. Dyera lowii) as tropical peat swamp forest restoration transplant species: effects of mycorrhizae and level of disturbance. Wetl. Ecol. Manag. 21, 307–321 (2013).Article 

Google Scholar  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

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