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Owing to drastic changes in the environment caused by human interference, wild mammals that are reservoirs of Leishmania have invaded residential areas where species of sand flies with eclectic feeding habits are found, and established a transmission cycle that eventually reaches humans23,24,25. In the study area, it was observed that the largest frequency of specimens over the years was captured in the residential environment, which are represented by residential and peridomicile areas. The lowest frequency was captured in the borders of the forest.The municipality of Paraty, located on the southern coast in the state of Rio de Janeiro, where the study was conducted, has many preserved areas of the Atlantic Forest and its climate is wet with no dry season13, which was confirmed during the three years of the present study, where the relative air humidity stayed high every month. The highest average rainfalls occur in summer and fall (autumn). The average temperature during the hottest months of the year was between approximately 25 °C and 26 °C, with a maximum of 31 °C, and in the coldest months, the temperature averaged between 20 and 21 °C, with a minimum of 16 °C, exhibiting an ideal environment for the activity of sand flies throughout the year.Barretto26 noted that atmospheric conditions, such as relative humidity, rainfall, and temperature directly influence the activity of these sand fly species. Migonemyia migonei and Ny. whitmani had lower activity at temperatures below 15 °C, Pi. fischeri below 10 °C, and Ny. intermedia at temperatures below 9.5 °C. The author also reported that heavy rains prevent sand flies from leaving their shelters; however, this can increase their density within residences, especially for species located next to residential areas. Light rain will not impede their activity, but in these conditions, they are not as frequently observed as they usually are. However, during rain periods, especially in the hot and humid summer period, the density of sand flies increases considerably.In the present study, four key vector species of Leishmania braziliensis Vianna, 1911, the etiologic agent of tegumentary leishmaniasis, were captured throughout the year. The most frequent was Ny. intermedia, followed by Pi. fischeri, Mg. migonei, and Ny. whitmani. Carvalho et al.27, in the State of Pernambuco, northeast region of Brazil, reported having found Mg. migonei infected with Leishmania infantum Nicolle, 1908, the etiologic agent of visceral leishmaniasis.According to Forattini28, there are sand fly species that are essentially resistant to climate changes throughout the seasons. Several are found, albeit in lower densities, during the cooler, dry months, while others disappear during this period. However, other factors also influence the incidence of sand flies in the same location, even under the same temperature and humidity conditions. Thus, to study the seasonality of sand fly species, it is important to perform systematized captures, for a period exceeding two years, to minimize the effects of these additional factors, for example, atypical years with a longer period of drought or humidity, more or less high temperatures, months with higher than expected rainfall or control measures applied by the municipality.In studies carried out in the Northeast region of Brazil, in a study carried out in the municipality of Codó, in the State of Maranhão, an inversely proportional correlation of the captured sandflies was observed in relation to relative air humidity, a direct correlation in relation to temperature and precipitation, a correlation directly proportional29. In the municipality of Sobral, State of Ceará, in the first year of the study, observed a negative correlation with temperature and a high positive correlation with humidity and precipitation, however, in the following year, there was no correlation between the density of captured sandflies and climatic variables30. The same occurred in this study, in the municipality of Paraty, in relation to relative air humidity and precipitation, but in relation to temperature, a strong positive correlation was obtained.In the studied area Ny. intermedia occurred in greater numbers in every month of the year, except in June and July, when it was less frequent than Pi. fischeri. The same pattern was observed for these two species, i.e., a gradual increase in abundance beginning in August, peak abundance in summer (January), followed by a decrease until winter (July). Brito et al.31, when researching the northern coast of the state of São Paulo, municipality of São Sebastião, noted the opposite, that Ny. intermedia had the highest abundance peaks during the driest and coldest period of the year, i.e., from May to August. However, the authors also emphasized the presence of this species throughout the year, mainly in the residential environment, and they stressed the importance of seasonal analyses for periods longer than a year.In the São Francisco River region, in the state of Minas Gerais, on the banks of the Rio Velhas, Saraiva et al.32, in a study over a two-year period, observed a different pattern. In the first year of study, after the rainy season from February to May, with high humidity and high temperature, Ny. intermedia was captured in greater numbers than during other months of the year. In the second year, peaks occurred in October, March, and June, with the highest peak in March, when there was elevated rainfall, high humidity, and high temperatures.In the state of Rio de Janeiro, in Serra dos Órgãos National Park, Aguiar and Soucasaux33 analyzed the monthly frequency in human bait and observed that Ny. fischeri was captured in every month except November. In the hot and humid period, from December to February, there was a gradual increase in the average abundances of this species, and then a slight decrease began in March and continued into April. During the cold and dry period of May and June, abundances started to increase, then decreased in July, and peaked in August. During August, Pi. fischeri was the dominant species of wildlife, and in September, abundances began to decline again.Mayo et al.34, studying the southeastern region of the state of São Paulo, observed that there was a seasonal trend in the abundance for species Mg. migonei, Ny. whitmani, Ny. intermedia, and Pi. fischeri, with abundance peaks recorded during the cooler, drier season (April to September) and low abundances during the warmer, wetter season (October to March). The authors revealed that the occurrence of intense fires in the study area in October, which caused severe environmental change, possibly interfered with the population dynamics of the species. In the present study, the opposite trend of seasonality was shown for the four key species, Ny. intermedia, Pi. fischeri, Mg. migonei, and Ny. whitmani, then what was observed by the above authors, the highest abundances occurred during the hottest period, increasing gradually until a maximum peak in January, and lowest abundances were seen during the coldest period, in July for the first three species, and in June for Ny. whitmani.In the neighboring municipality of this study in Angra dos Reis, in the Ilha Grande, Carvalho et al.35 reinforced the epidemiological importance of Ny. intermedia in the State of Rio de Janeiro and highlighted the role of Mg. migonei in the transmission of cutaneous leishmaniasis with its high rate of infection natural by Leishmania. Still in the same region, along the southern coast of the State of Rio de Janeiro, Aguiar et al.8 conducted systematic catches for two years, with the aim being to analyze the monthly frequency of sand flies in residential and forest environments. The authors discovered results like what occurred in this study in Paraty, that the four most important species caught, Ny. intermedia, Pi. fischeri, Mg. migonei, and Ny. whitmani, had higher average numbers during the hot and humid period of the year, i.e., between October and January, with a maximum peak in December for Ny. intermedia and Pi. fischeri, and January for Mg. migonei. The prevalence of Ny. intermedia was evident in every month, both inside the residence and around the residential area. In the colder and drier season, from May to August, there was a balance with Pi. fischeri, but from August, inside the residence, and from September, around the residence, the frequency increased until it reached its peak in December. There was a gradual increase in the frequency of this species in the warmer and wetter period (between October and January), with average temperatures ranging from 26 to 29 °C and relative air humidity between 84 and 87%.Condino et al.36, when studying the southwestern region of the state of São Paulo, observed that Ny. intermedia and Ny. whitmani had the highest frequencies during the months of May, September, and December with temperatures ranging from 21 to 25.7 °C and rainfall between 66.7 and 195.1 mm. In June, the lowest frequency of sand flies was observed, which then increased until a maximum peak in September. Temperature data and rainfall index were not correlated with the density of specimens, especially as the study was carried out over only one year. In this study, the opposite was observed for Ny. intermedia and Ny. whitmani in the month of May, one of the months with the lowest density.In the city of Petrópolis, state of Rio de Janeiro, Souza et al.24 observed a prevalence of Ny. intermedia and Ny. whitmani, with the latter species prevailing around the residence. Migonemyia migonei and Pi. fischeri were also present but to a lesser extent. In the forest, Ny. whitmani was more abundant, followed by Pi. fischeri, while Ny. intermedia was found at lower abundances. However, Ny. intermedia and Pi. fischeri were present during every month of the year. The authors also found a significant correlation between the number of sand flies and environmental changes such as temperature, relative humidity, and rainfall. The same was observed, in this study, in the forest with Ny. intermedia, however, in this environment the number of Pi. fischeri specimens was higher than that of Ny. whitmani.In the north of Espírito Santo, Virgens et al.37 observed that Ny. intermedia was present in almost every month of the study period, with peaks in the warmer and wetter months. The authors highlighted that the low numbers of this species were recorded during and after high rainfall periods, suggesting that heavy rain is unfavorable for the development of immature forms, as breeding sites in altered habitats suffered a greater impact because of extreme weather conditions.In a study carried out by Guimarães et al.38 to observe the competence of Mg. Migonei to Leishmania infantum, concluded that this species is highly susceptible to the development of this parasite and that in addition to its anthropophilia and abundance in areas with an active focus of visceral leishmaniasis, it can act as a vector of this disease in Latin America.In the studied area, Ny. intermedia, one of the main vectors of the etiological agent of tegumentary leishmaniasis in the region2, was present in significant numbers in the home environment throughout all months of the year. The species Pi. fischeri was present over the months in expressive numbers in all types and locations of capture, that is, both in the environment altered by human activity and in the natural environment where leishmaniasis occurs in its natural enzootic cycle. Migonemyia migonei, present throughout the year in the peridomestic environment, showed its association with the dog, where it was prevalent throughout the year in the kennel, being an important vector of the etiological agent of tegumentary leishmaniasis, as well as being suspected in areas of visceral leishmaniasis transmission, where the main vector of this disease is not found. And Ny. whitmani present in the peridomicile, mainly in the hottest months of the year, in addition to the forest and forest margins, it was observed that in this study region the species is emerging through a selective process of adaptation in environments that were negatively affected by the increase of human activity. Thus, despite observing a period of greater frequency of sand flies in the hottest months of the year, a period with high rainfall, the high relative humidity is observed throughout the year, as well as the presence of species of epidemiological importance Ny. intermedia, Pi. fischeri, Mg. migonei and Ny. whitmani, who are involved in the propagation of the etiological agent of tegumentary leishmaniasis to humans and animals, causing greater contact between the region’s inhabitants with these dipterans and thus, a greater risk of contracting the disease. More

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Study site, tree selections, and drought-simulation experimentThis research was performed in Guangzhou (22°26′-23°56′N, 112°57′-114°03′E), which is a core city located in subtropical zones. With an area of 7434.4 km2 and a population of 18.87 million, Guangzhou’s urbanization rate has reached 86.46%. To cope with multiple environmental challenges, several urban-forest nurseries were established to cultivate and introduce various tree species. Among them, we selected the one in Tianhe District as our study site. This nursery was not only abundant with native and exotic tree species but also equipped with similar edatope in cities, which was ideal for our research.Tilia cordata Mill. (Tc) and Tilia tomentosa Moench (Tt), originating from the west of Britain and southeast of Europe, were common urban tree species planted in European cities. Based on their performance in providing ecological and landscape functions, these two tree species were considered to be introduced for urban greening. Therefore, Tilia cordata Mill. (Tc) and Tilia tomentosa Moench (Tt) were selected as our objectives, which were investigated for their growth and ecosystem services to evaluate their adaption in Guangzhou. In addition, a native tree species Tilia miqueliana Maxim (Tm) was also implemented concurrent measurement as a comparison.For each of the three surveyed tree species, ten trees with a diameter at breast height (DBH) around 5.5 cm and tree height around 2.5 m were chosen for our experiment, which were thought to possess similar initial statuses. To investigate the impact of drought on the growth and ecosystem services of the three selected tree species, a controlled experiment was launched from January to December in 2020. For each tree species, five trees were planted in the common environment as the controlled group, while the other five trees were under the precipitation-exclusion installation (PEI) as the drought-simulation group. Consisting of several water-proof tents, PEI was adequately large and could completely prevent trees from obtaining rainfalls, which created a precipitation-exclusive environment to simulate an enduring drought event within the whole research period (Fig. 1).Figure 1Schematic diagram of the drought simulation experiment for the three surveyed tree species.Full size imageEnvironmental monitoring systemsClimatic data were sampled every 10 min with a weather station (WP3103 mesoscale automatic weather station, China) located at an unshaded site in the nursery. The data were stored in the logger and copied to our laboratory to produce daily or monthly data. All the climatic variables, including photosynthetically active radiation (PAR, µmol m-2 s-1), wind speed (m s-1), precipitation (mm), and air temperature (°C) were calculated from January to December in 2020.For volumetric soil water content (%; VWC), the HOBO MX2307 system (Onetemp, Adelaide, Australia), placed in a shaded box in the nursery, was applied for all the three tree species from both the controlled and drought-simulation groups. For each individual tree, the sensing probe was inserted horizontally at the depths of 30 cm and located 20 cm in the northern direction from the tree stems. Based on the daily readings, monthly means were calculated from January to December in 2020.Measurement of above-ground growthTo investigate the above-ground growth of the three tree species from both the controlled and drought-simulation groups, their DBH (diameter at breast height, cm), tree height (m), and LAI (leaf area index) were measured at the beginning of each month in 2020. DBH was measured with the help of a caliper (Altraco Inc., Sausalito, California, USA), and their tree heights were measured using a standard tape. The crown analytical instrument CI-110 (Camas, Washington State, USA) was used to capture an accurate image of tree crowns and calculate LAI. Sufficient numbers of points were measured and recorded to describe each tree’s average crown shape. The software FV2200 (LICOR Biosciences, Lincoln, NE) helped compute each tree’s crown width and crown area.Measurement of below-ground growthFine root coring campaigns were launched for all the trees of the three tree species from both the controlled and drought treatment groups every three to four months, i.e., in February, May, September, and December. Although the coring campaign might damage part of the roots, the fine roots obtained each time were a mere portion of the whole root system, not affecting the general development of trees’ underground processes. For every individual tree, two 30-cm soil cores were applied in each direction of north, south, east, and west, of which one was located at 20 cm to the trunk (paracentral roots) and the other one was located at 40 cm (outer roots). In addition, the soil samples were evenly divided into three horizons which were 0–10 cm (shallow layer), 10–20 cm (middle layer), and 20–30 cm (deep layer). Then a sieve with 2-mm mesh size was used to filter all the fine roots. The fine roots were washed carefully to remove the adherent soils and dried in an oven at 65 ℃ for 72 h. Finally, all the samples were weighed using a balance with an accuracy of four decimal places to obtain the dry weight. The fine root biomass at different depths was calculated using the dry weight divided by the cross-sectional area of the auger20.Model’s simulation of ecosystem servicesThe process-based model City-Tree was used to predict the ecosystem services of the three tree species from both the controlled and drought-simulation groups23. The model required the data of tree growth parameters including tree height, DBH, and crown area together with environmental conditions such as edaphic and climatic data24. In this research, cooling, evapotranspiration and CO2 fixation of the three surveyed tree species in the controlled and drought-treatment groups were simulated at the end of 2020.The actual evapotranspiration eta was calculated from the potential evapotranspiration using fetp[t], Tilia’s factors fetp[t], and the reduction factor fred:$${mathrm{et}}_{mathrm{a}}={mathrm{f}}_{mathrm{red}}*{mathrm{f}}_{mathrm{etp}}left[mathrm{t}right]*{mathrm{et}}_{mathrm{p}}$$The process of tree’s evapotranspiration (etp) was calculated on the basis of SVAT algorithm together with Penman formula in the module on water balance as below:$${mathrm{et}}_{mathrm{p}}=left[mathrm{s }/ left(mathrm{s}+upgamma right)right]*left({mathrm{r}}_{mathrm{s}}-{mathrm{r}}_{mathrm{L}}right) /mathrm{ L}+left[1-mathrm{s }/ left(mathrm{s}+upgamma right)right]*{mathrm{e}}_{mathrm{s}}*mathrm{f }left({mathrm{v}}_{mathrm{u}}right)$$with γ: psychrometric constant in hPa K−1; s: the slope of the saturation vapour pressure curve in hPa K−1; rs: short wave radiation balance in W m−2; rL: long-wave radiation balance in W m−2; L: specific evaporation heat in W m−2 mm−1 d; es: saturation deficit in hPa; f (vu): ventilation function with vu being the daily average wind speed in m s−1.Within the module cooling, the energy needed for the transition of water from liquid to gaseous phase was calculated based on the crown area (CA) and the transpiration eta sum:$${mathrm{E}}_{mathrm{A}}= {mathrm{et}}_{mathrm{a}}*mathrm{CA}-left({mathrm{L}}_{mathrm{O}}* -0.00242*mathrm{temp}right) / {mathrm{f}}_{mathrm{con}}$$with EA: energy released by a tree through transpiration (kWh tree-1), LO: energy needed for the transition of the 1 kg of water from the liquid to gaseous phase = 2.498 MJ (kgH2O)-1 and temp = temperature in ℃, fcon: 0.5.The calculation of new assimilation in the module of photosynthesis and respiration was on the basis of the approach of Haxeltine and Prenticem25. The model assumed that 50% of the incoming short-wave radiation is photosynthetic active radiation (PAR). Using the LAI and a light extinction factor of 0.5, the radiation amount of 1 m2 leaf area can be estimated based on an exponential function according to the Lambert–Beer law. This way, the gross assimilation per m2 leaf area as the daily mean of the month can be derived from:$${text{A}} = {text{d}}*{{left[ {left( {{text{J}}_{{text{p}}} + {text{J}}_{{text{r}}} – {text{sqrt}} left( {left( {{text{J}}_{{text{P}}} + {text{J}}_{{text{r}}} } right)^{2} – 4*uptheta *{text{J}}_{{text{p}}} *{text{J}}_{{text{r}}} } right)} right)} right]} mathord{left/ {vphantom {{left[ {left( {{text{J}}_{{text{p}}} + {text{J}}_{{text{r}}} – {text{sqrt}} left( {left( {{text{J}}_{{text{P}}} + {text{J}}_{{text{r}}} } right)^{2} – 4*uptheta *{text{J}}_{{text{p}}} *{text{J}}_{{text{r}}} } right)} right)} right]} {left( {2*uptheta } right)}}} right. kern-0pt} {left( {2*uptheta } right)}}$$with A: gross assimilation [g C m−2 d−1]; d: mean day length of the month [h]; Jp: reaction of photosynthesis on absorbed photosynthetic radiation [g C m−2 h−1]; Jr: rubisco limited rate of photosynthesis [g C m−2 h−1]; θ: form factor = 0.7.Jp was defined as a function of the photosynthetic active radiation PAR in mol m−2 h−1 and the efficiency of carbon fixation per absorbed PAR [g C mol−1].$${text{J}}_{{text{p}}} = {text{c}}_{{text{p}}} {text{*PAR}}$$$${text{c}}_{{text{p}}} = alpha *left( {{text{p}}_{{{text{ci}}}} – {text{r}}} right){ /}left( {{text{p}}_{{{text{ci}}}} – {text{r}}} right)*gamma *{text{m}}_{{{text{co}}_{2} }} *{text{i}}left[ {text{t}} right]$$with α: intrinsic quantum efficiency for CO2 uptake = 0.08; pci: partial pressure of the internal CO2 [Pa]; r: CO2 compensation point [Pa]; ϒ: species dependent adjustment function for tree age; m CO2: molecular mass of C = 12.0 g mol−1; i[t]: influence of temperature on efficiency.Net assimilation AN [g C m−2 d−1] was then derived from the gross assimilation A and the dark respiration Rd by:$${text{A}}_{{text{N}}} = {text{A}} – {text{R}}_{{text{d}}}$$$${text{R}}_{{text{d}}} =upbeta *{text{V}}_{{text{m}}}$$where Vm was calculated as:$${text{V}}_{{text{m}}} = {1 mathord{left/ {vphantom {1 upbeta }} right. kern-0pt} upbeta } * {{{text{c}}_{{text{p}}} } mathord{left/ {vphantom {{{text{c}}_{{text{p}}} } {{text{c}}_{{text{r}}} * {text{PAR}} * left[ {left( {2uptheta – 1} right) * beta * {{text{d}} mathord{left/ {vphantom {{text{d}} {{text{d}}_{max } }}} right. kern-0pt} {{text{d}}_{max } }} – left( {2uptheta *upbeta *{{text{d}} mathord{left/ {vphantom {{text{d}} {{text{d}}_{max } }}} right. kern-0pt} {{text{d}}_{max } }} – {text{c}}_{{text{r}}} } right)*varsigma } right]}}} right. kern-0pt} {{text{c}}_{{text{r}}} * {text{PAR}} * left[ {left( {2theta – 1} right) * beta * {{text{d}} mathord{left/ {vphantom {{text{d}} {{text{d}}_{max } }}} right. kern-0pt} {{text{d}}_{max } }} – left( {2theta *upbeta *{{text{d}} mathord{left/ {vphantom {{text{d}} {{text{d}}_{max } }}} right. kern-0pt} {{text{d}}_{max } }} – {text{c}}_{{text{r}}} } right)*varsigma } right]}}$$By multiplying AN, the number of days and the total leaf area, the entire monthly net assimilation of the tree can be obtained. In this study, we assumed a fixed share of 50% as respiration based on the gross primary production that the resulting net primary production NPP was transformed in the content of fixed carbon by multiplying the value with the carbon conversion factor 0.524.$${mathrm{Carbon}}_{mathrm{fix}}=0.5*mathrm{NPP}$$Statistical analysesThe software package R was used for statistical analysis. To investigate the differences between means, two-sampled t-test and analysis of variance (ANOVA) with Tukey’s HSD (honestly significant difference) test were used. All the cases, the means were reported as significant when P  More

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EthicsNo ethics approval was required for this study. Our study was conducted in a state-owned industrial oil palm plantation where we established a cooperation with the estate owner to access the site and collect data. No endangered or protected species were sampled. Research permits were obtained from the Ministry of Research, Technology and Higher Education, and sample collection and sample export permits were obtained from the Ministry of Environment and Forestry of the Republic of Indonesia.Study area and experimental designOur study was conducted in a state-owned industrial oil palm plantation (PTPN VI) located in Jambi, Indonesia (1.719° S, 103.398° E, 73 m above sea level). Initial planting of oil palms within the 2,025 ha plantation area started in 1998 and ended in 2002; planting density was 142 palms ha−1, spaced 8 m apart in each row and between rows, and palms were ≥16 years old during our study period of 2016–2020. The study sites have a mean annual temperature of 27.0 ± 0.2 °C and a mean annual precipitation of 2,103 ± 445 mm (2008–2017, Sultan Thaha Airport, Jambi). The management practices in large-scale oil palm plantations typically result in three contrasting management zones: (1) a 2 m radius around the base of the palm that was weeded (four times a year) and raked before fertilizer application, hereafter called the ‘palm circle’; (2) an area occurring every second inter-row, where pruned senesced palm fronds were piled up, hereafter called ‘frond piles’; and (3) the remaining area of the plantation where less weeding (two times a year) and no fertilizer were applied, hereafter called ‘inter-rows’.Within this oil palm plantation, we established a management experiment in November 2016 with full factorial treatments of two fertilization rates × two weeding practices: conventional fertilization rates at PTPN VI and other large-scale plantations (260 kg N–50 kg P–220 kg K ha−1 yr−1), reduced fertilization rates based on quantified nutrient export by harvest (136 kg N–17 kg P ha−1 yr−1–187 kg K ha−1 yr−1), herbicide and mechanical weeding15. The reduced fertilization treatment was based on quantified nutrient export from fruit harvest, calculated by multiplying the nutrient content of fruit bunches with the long-term yield data of the plantation. Fertilizers were applied yearly in April and October following weeding and raking of the palm circle. The common practice at PTPN VI and other large-scale plantations on acidic Acrisol soils is to apply lime and micronutrients, and these were unchanged in our management experiment. Before each N–P–K fertilizer application, dolomite and micronutrients were applied to the palm circle in all treatment plots using the common rates)52: 426 kg ha−1 yr−1 dolomite and 142 kg Micro-Mag ha−1 yr−1 (containing 0.5% B2O3, 0.5% CuO, 0.25% Fe2O3, 0.15% ZnO, 0.1% MnO and 18% MgO). Herbicide treatment was carried out using glyphosate in the palm circle (1.50 l ha−1 yr−1, split into four applications per year) and in the inter-rows (0.75 l ha−1 yr−1, split into two applications per year). Mechanical weeding was done using a brush cutter in the same management zones and at the same frequency as the herbicide treatment.The 22 factorial design resulted in four treatment combinations: conventional fertilization with herbicide treatment, reduced fertilization with herbicide treatment, conventional fertilization with mechanical weeding and reduced fertilization with mechanical weeding. The four treatments were randomly assigned on 50 m × 50 m plots replicated in four blocks, totalling 16 plots. The effective measurement area was the inner 30 m × 30 m area within each replicate plot to avoid any possible edge effects. For indicators (below) that were measured within subplots, these subplots were distributed randomly within the inner 30 m × 30 m of a plot. All replicate plots were located on flat terrain and on an Acrisol soil with a sandy clay loam texture.Ecosystem functions and multifunctionalityOur study included multiple indicators for each of the eight ecosystem functions23, described in details below (Supplementary Tables 1 and 2). All the parameters were expressed at the plot level by taking the means of the subplots (that is, biological parameters) or the area-weighted average of the three management zones per plot (that is, soil parameters). (1) Greenhouse gas (GHG) regulation was indicated by NEP, soil organic C (SOC) and soil GHG fluxes. (2) Erosion prevention was signified by the understory vegetation cover during the four-year measurements. (3) Organic matter decomposition was indicated by leaf litter decomposition and soil animal decomposer activity. (4) Soil fertility was signified by gross N mineralization rate, effective cation exchange capacity (ECEC), base saturation and microbial biomass N. (5) Pollination potential was designated by pan-trapped arthropod abundance and nectar-feeding bird activity. As such, it does not quantify the pollination potential for oil palm, which is mainly pollinated by a single weevil species, but rather as a proxy for a general pollination potential for other co-occurring plants. (6) Water filtration (the capacity to provide clean water) was indicated by leaching losses of the major elements. (7) Plant refugium (the capacity to provide a suitable habitat for plants) as signified by the percentage ground cover of invasive plants to the total ground cover of understory vegetation during the four-year measurements. (8) Biological control (the regulation of herbivores via predation) was indicated by insectivorous bird and bat activities and the soil arthropod predator activity.All the ecosystem functions were merged into a multifunctionality index using the established average and threshold approaches12. For average multifunctionality, we first averaged the z-standardized values (Statistics) of indicators for each ecosystem function and calculated the mean of the eight ecosystem functions for each plot. For threshold multifunctionality, this was calculated from the number of functions that exceeds a set threshold, which is a percentage of the maximum performance level of each function12; we investigated the range of thresholds from 10% to 90% to have a complete overview. The maximum performance was taken as the average of the three highest values for each indicator per ecosystem function across all plots to reduce effect of potential outliers. For each plot, we counted the number of indicators that exceeded a given threshold for each function and divided by the number of indicators for each function12.Indicators of GHG regulationWe calculated annual NEP for each plot as: net ecosystem C exchange – harvested fruit biomass C (ref. 16), whereby net ecosystem C exchange = Cout (or heterotrophic respiration) – Cin (or net primary productivity)53. The net primary productivity of oil palms in each plot was the sum of aboveground biomass production (aboveground biomass C + frond litter biomass C input + fruit biomass C) and belowground biomass production. Aboveground biomass production was estimated using allometric equations developed for oil palm plantations in Indonesia54, using the height of palms measured yearly from 2019 to 2020. Annual frond litter biomass input was calculated from the number and dry mass of fronds pruned during harvesting events of an entire year in each plot and was averaged for 2019 and 2020. Aboveground biomass production was converted to C based on C concentrations in wood and leaf litter55. Annual fruit biomass C production (which is also the harvest export) was calculated from the average annual yield in 2019 and 2020 and the measured C concentrations of fruit bunches. Belowground root biomass and litter C production were taken from previous work in our study area55, and it was assumed constant for each plot. Heterotrophic respiration was estimated for each plot as: annual soil CO2 C emission (below) × 0.7 (based on 30% root respiration contribution to soil respiration from a tropical forest in Sulawesi, Indonesia56) + annual frond litter biomass C input × 0.8 (~80% of frond litter is decomposed within a year in this oil palm plantation8). SOC was measured in March 2018 from composite samples collected from two subplots in each of the three management zones per plot down to 50 cm depth. Soil samples were air dried, finely ground and analysed for SOC using a CN analyser (Vario EL Cube, Elementar Analysis Systems). SOC stocks were calculated using the measured bulk density in each management zone, and values for each plot were the area-weighted average of the three management zones (18% for palm circle, 15% for frond piles and 67% for inter-rows)15,22.From July 2019 to June 2020, we conducted monthly measurements of soil CO2, CH4 and N2O fluxes using vented, static chambers permanently installed in the three management zones within two subplots per plot11,57. Annual soil CO2, CH4 and N2O fluxes were trapezoidal interpolations between measurement periods for the whole year, and values for each plot were the area-weighted average of the three management zones (above).Indicators of erosion preventionDiversity and abundance of vascular plants were assessed once a year from 2016 to 2020 before weeding in September–November. In five subplots per plot, we recorded the occurrence of all vascular plant species and estimated the percent cover of the understory vegetation. The percentage cover and plant species richness of each measurement year were expressed in ratio to that of 2016 to account for initial differences among the plots before the start of the experiment. For example, percentage cover in 2017 was:$$mathrm{Cover}_{2017} = frac{{left( {mathrm{Cover}_{2017} – mathrm{Cover}_{2016}} right)}}{{mathrm{Cover}_{2016}}}$$The values from five subplots were averaged to represent each plot.Indicators of organic matter decompositionLeaf litter decomposition was determined using litter bags (20 cm × 20 cm with 4 mm mesh size) containing 10 g of dry oil palm leaf litter8. Three litter bags per plot were placed on the edge of the frond piles in December 2016. After eight months of incubation in the field, we calculated leaf litter decomposition as the difference between initial litter dry mass and litter dry mass following incubation. Soil animal decomposer activity is described below (Soil arthropods).Indicators of soil fertilityAll these indicators were measured in February–March 2018 in the three management zones within two subplots per plot22. Gross N mineralization rate in the soil was measured in the top 5 cm depth on intact soil cores incubated in situ using the 15N pool dilution technique58. ECEC and base saturation were measured in the top 5 cm depth as this is the depth that reacts fast to changes in management22. The exchangeable cation concentrations (Ca, Mg, K, Na, Al, Fe, Mn) were determined by percolating the soil with 1 mol l−1 of unbuffered NH4Cl, followed by analysis of the percolates using an inductively coupled plasma-atomic emission spectrometer (ICP-AES; iCAP 6300 Duo view ICP Spectrometer, Thermo Fisher Scientific). Base saturation was calculated as the percentage exchangeable bases (Mg, Ca, K and Na) on ECEC. Microbial biomass N was measured from fresh soil samples using the fumigation-extraction method59. The values for each plot were the mean of the two subplots that were the area-weighted average of the three management zones (above)15,22.Indicators of general pollination potentialFluorescent yellow pan traps were used to sample aboveground arthropods (to determine pollinator communities60) in November 2016, September 2017 and June 2018. The traps were attached to a platform at the height of the surrounding vegetation within a 2 × 3 grid centred in the inter-rows of each plot in six clusters of three traps, totalling 18 traps per plot. Traps were exposed in the field for 48 h. We stored all trapped arthropods in 70% ethanol and later counted and identified to order and species level. The abundance of trapped arthropods in 2017 and 2018 were calculated as the ratio to the abundance in 2016 to account for initial differences among the plots before the start of the experiment. The activity of nectar-feeding birds is described below (Birds and bats).Indicators of water filtrationElement leaching losses were determined from analyses of soil-pore water sampled monthly at 1.5 m depth using suction cup lysimeters (P80 ceramic, maximum pore size 1 μm; CeramTec) over the course of one year (2017–2018)15. Lysimeters were installed in the three management zones within two subplots per plot. Dissolved N was analysed using continuous flow injection colorimetry (SEAL Analytical AA3, SEAL Analytical), whereas these other elements were determined using ICP-AES. The values for each plot were the mean of the two subplots that were the area-weighted average of the three management zones15,22.Indicators of plant refugiumIn five subplots per plot, the percentage cover and species richness of invasive understory plant species were assessed once a year from 2016 to 2020 before weeding in September–November. We defined invasive species as those plants non-native to Sumatra61 and among the ten dominant species (excluding oil palm) in the plantation for each year. The percentage cover of invasive understory plant species of each measurement year was expressed in a ratio to that of 2016 to account for initial differences among the plots before the start of the experiment. The values for each plot were represented by the average of five subplots.Indicators of biological controlThe activities of insectivorous birds and bats are described below (Birds and bats). In five subplots per plot, soil invertebrates were collected (Soil arthropods), counted, identified to taxonomic order level and subsequently classified according to their trophic groups that include predators60. The values from five subplots were average to represent each plot.BiodiversityBiodiversity was measured by the taxonomic richness of seven multitrophic groups, described in details below (Supplementary Tables 1 and 2).Understory plant species richnessThe method is described above (Indicators of erosion prevention), using the number of species as an indicator (Supplementary Table 2).Soil microorganism richnessThis was determined in May 2017 by co-extracting RNA and DNA from three soil cores (5 cm diameter, 7 cm depth) in five subplots per plot62. While DNA extraction describes the entire microbial community, RNA represents the active community. The v3–v4 region of the 16S rRNA gene was amplified and sequenced with a MiSeq sequencer (Illumina). Taxonomic classification was done by mapping curated sequences against the SILVA small subunit (SSU) 138 non-redundant (NR) database63 with the Basic Local Alignment Search Tool (BLASTN)64.Soil arthropod order richnessFor determination of soil arthropods, we collected soil samples (16 cm × 16 cm, 5 cm depth) in five subplots per plot in October–November 2017. We extracted the animals from the soil using a heat-gradient extractor65, collected them in dimethyleneglycol-water solution (1:1) and stored in 80% ethanol. The extracted animals were counted and identified to taxonomic order level61. They were also assigned to the trophic groups decomposers, herbivores and predators based on the predominant food resources recorded in previous reviews and a local study66,67. Orders with diverse feeding habits were divided into several feeding groups, for example, Coleoptera were divided into mostly predatory families (Staphylinidae, Carabidae), herbivorous families (for example, Curculionidae) and decomposer families (for example, Tenebrionidae). The total number of individuals per taxonomic group in each subplot was multiplied by the group-specific metabolic rate, which were summed to calculate soil animal decomposer activity. The values from five subplots were average to represent each plot.Aboveground arthropod order and insect family richnessIn addition to the fluorescent yellow pan traps described above (Indicators of general pollination potential), sweep net and Malaise trap samplings were conducted in June 2018, which targeted the general flying and understory dwelling arthropod communities. Sweep net sampling was conducted within the understory vegetation along two 10 m long transects per plot, with ten sweeping strokes performed per transect. In each plot, we installed a single Malaise trap between two randomly chosen palms and exposed it for 24 h. Arthropods were counted, identified to taxonomic order level and the insects to taxonomic family level and values from the three methods were summed to represent each plot.Birds and batsBirds and bats passing at each replicate plot were sampled in September 2017 using SM2Bat + sound recorders (Wildlife Acoustics) with two microphones (SMX-II and SMX-US) placed at a height of 1.5 m in the middle of each plot68. We assigned the bird vocalization to species with Xeno-Canto69 and the Macaulay library70. Insectivorous bat species richness was computed by dividing them into morphospecies based on the characteristics of their call (call frequency, duration, shape). In addition, we gathered information on proportional diet preferences of the bird species using the EltonTrait database71. We defined birds feeding on invertebrates (potential biocontrol agents) as the species with a diet of at least 80% invertebrates and feeding on nectar (potential pollinators), if the diet included at least 20% of nectar.Economic indicatorsWe used six indicators linked to the level and stability of yield and profit: yield, lower fifth quantile of the yield per palm per plot, shortfall probability, management costs, profit and relative gross margin. We assessed fruit yield by weighing the harvested fruit bunches from each palm within the inner 30 m × 30 m area of each plot. The harvest followed the schedule and standard practices of the plantation company: each palm was harvested approximately every ten days and the lower fronds were pruned. For each plot, we calculated the average fruit yield per palm and scaled up to a hectare, considering the planting density of 142 palms per ha. Because the palms in each plot have different fruiting cycles and were harvested continuously, the calculation of an annual yield may lead to misleading differences between treatments. Therefore, we calculated the cumulative yield from the beginning of the experiment to four years (2017–2020), which should account for the inter- and intra-annual variations in fruit production of the palms in the plots and thus allowing for comparison among treatments. As effects of management practices on yield may be delayed46, we also calculated the cumulative yield during two consecutive years (2017–2018 and 2019–2020) and checked for treatment effects on yield and profit indicators separately for these two periods.We computed risk indicators on the cumulative yield and on the yield between the two periods. We used the lowest fifth quantile of the yield per palm per plot (left side of the distribution) to indicate the production of the palms with lowest performance. Also, we determined the yield shortfall probability (lower partial moment 0th order), defined as the share of palms that fell below a predefined threshold of yield; the thresholds chosen were 630 kg−1 per palm for cumulative yield and 300 kg−1 per palm per year for the two-year yield, which corresponded to 75% of the average yield.Revenues and costs were calculated as cumulative values during four years of the experiment (2017–2020) using the same prices and costs for all the years. This was because we were interested in assessing the economic consequences of different management treatments, and they might be difficult to interpret when changes in prices and costs between calendar years are included, which are driven by external market powers rather than the field-management practices. For the same reason, we abstained from discounting profits. Given the usually high discount rates applied to the study area, slight differences in harvesting activities between calendar years or months might lead to high systematic differences between the management treatments, which are associated with the variation in work schedule within the plantation rather than the actual difference among management treatments. Revenues were calculated from the yield and the average price of the fruit bunches in 2016 and 201761. Material costs were the sum of the costs of fertilizers, herbicide and gasoline for the brush cutter. Labour costs were calculated from the minimum wage in Jambi and the time (in labour hours) needed for the harvesting, fertilizing and weeding operations, which were recorded in 2017 for each plot. The weeding labour included the labour for raking the palm circle before fertilization, which was equal in all treatments, and the weeding in the palm circle and inter-rows either with herbicide or brush cutter. In addition, we included the time to remove C. hirta, which must be removed mechanically from all plots once a year, calculated from the average weed-removal time in the palm circle and the percentage cover of C. hirta in each plot for each year. We then calculated the profit as the difference between revenues and the total management costs and the relative gross margin as the gross profit proportion of the revenues.StatisticsTo test for differences among management treatments for each ecosystem function and across indicators of biodiversity, the plot-level value of each indicator was first z standardized (z = (actual value − mean value across plots) / standard deviation)4. This prevents the dominance of one or few indicators over the others, and z standardization allows several distinct indicators to best characterize an ecosystem function or biodiversity4. Standardized values were inverted (multiplied by −1) for indicators of which high values signify undesirable effect (that is, NEP, soil N2O and CH4 fluxes, element leaching losses, invasive plant cover, yield shortfall, management costs) for intuitive interpretations. For a specific ecosystem function (Supplementary Figs. 1 and 2) and across indicators of biodiversity (Fig. 2), linear mixed-effects (LME) models were used to assess differences among management treatments (fertilization, weeding and their interaction) as fixed effects with replicate plots and indicators (Supplementary Tables 2 and 3) as random effects. The significance of the fixed effects was evaluated using ANOVA72. The LME model performance was assessed using diagnostic residual plots73. As indicator variables may systematically differ in their responses to management treatments, we also tested the interaction between indicator and treatment (Table 1). For testing the differences among management treatments across ecosystem functions (that is, multifunctionality; Fig. 1), we used for each replicate plot the average of z-standardized indicators of each ecosystem function and ranges of thresholds (that is, number of functions that exceeds a set percentage of the maximum performance of each function12; Supplementary Fig. 3). The LME models had management treatments (fertilization, weeding and their interaction) as fixed effects and replicate plots and ecosystem functions as random effects; the interaction between ecosystem function and treatment were also tested to assess if there were systematic differences in their responses to management treatments (Table 1). As we expected that the type of weeding will influence ground vegetation, we tested for differences in ground cover of understory vegetation, measured from 2016 to 2020, using LME with management treatments as fixed effect and replicate plots and year as random effects. Differences among management treatments (fertilization, weeding and their interaction) in yield and profit indicators, which were cumulative values over four years (Fig. 3) or for two separate periods (2017–2018 and 2019–2020; Supplementary Fig. 4), were assessed using linear model ANOVA (Table 1). For clear visual comparison among management treatments across ecosystem functions, multitrophic groups for biodiversity, and yield and profit indicators, the fifth and 95th percentiles of their z-standardized values were presented in a petal diagram (Fig. 4 and Supplementary Fig. 5). Data were analysed using R (version 4.0.4), using the R packages ‘nlme’ and ‘influence.ME’73.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More