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An understanding of multi-annual behaviour in streamflow allows for better estimation of the risks associated with hydrological extremes. This can enable improved preparedness for streamflow-dependant services, such as freshwater ecology, drinking water supply and agriculture. Recently, efforts have focused on detecting relationships between long-term hydrological behaviour and oscillatory climate systems (such as the North Atlantic Oscillation – NAO). For instance, the approximate 7 year periodicity of the NAO has been detected in groundwater-level records in the North Atlantic region, providing potential improvements to the preparedness for future water resource extremes due to their repetitive, periodic nature. However, the extent to which these 7-year, NAO-like signals are propagated to streamflow, and the catchment processes that modulate this propagation, are currently unknown. Here, we show statistically significant evidence that these 7-year periodicities are present in streamflow (and associated catchment rainfall), by applying multi-resolution analysis to a large data set of streamflow and associated catchment rainfall across the UK. Our results provide new evidence for spatial patterns of NAO periodicities in UK rainfall, with areas of greatest NAO signal found in southwest England, south Wales, Northern Ireland and central Scotland, and show that NAO-like periodicities account for a greater proportion of streamflow variability in these areas. Furthermore, we find that catchments with greater subsurface pathway contribution, as characterised by the baseflow index (BFI), generally show increased NAO-like signal strength and that subsurface response times (as characterised by groundwater response time – GRT), of between 4 and 8 years, show a greater signal presence. Our results provide a foundation of understanding for the screening and use of streamflow teleconnections for improving the practice and policy of long-term streamflow resource management.

The drive for farm businesses to move towards net zero greenhouse gas emissions means that there is a need to develop robust methods to quantify the amount of biomass carbon (C) on farms. Direct measurements can be destructive and time-consuming and some prediction methods provide no assessment of uncertainty. This study describes the development, validation, and use of an integrated spatial approach, including the use of lidar data, and Bayesian Belief Networks (BBNs) to quantify total biomass carbon stocks (Ctotal) of i) land cover and ii) landscape features such as hedges and lone trees for five case study sites in lowland England. The results demonstrated that it was possible to develop and use a remote integrated approach to estimate biomass carbon at a farm scale. The highest achievable prediction accuracy was attained from models using the variables AGBC, BGBC, DOMC, age, height, species and land cover, derived from measured information and from literature review. The two BBN models successfully predicted the test values of the total biomass carbon with propagated error rates of 6.7 % and 4.3 % for the land cover and landscape features respectively. These error rates were lower than in other studies indicating that the seven predictors are strong determinants of biomass carbon. The lidar data also enabled the spatial presentation and calculation of the variable C stocks along the length of hedges and within woodlands. Natural Environment Research Council (NERC): NE/L002493/1

Few studies to date have investigated the effect of uncertainty in soil property estimates from spectroscopy on soil management. In this study we considered the implications for variable rate application of phosphorus (P) and potassium (K) fertiliser. First, the uncertainty in soil available P and K estimates from spectroscopy was quantified as a function of the calibration set size at the field-scale.Based on the observed variation in P and K in four experimental fields, we simulated 100 realisations per field for an in silico experiment. To simulate the process of sampling soil and predicting fertiliser requirement, we performed sampling on our simulated fields using a spatial coverage design. We added a calibration error to each sample value to simulate the error associated with spectroscopic prediction. Kriging was used to estimate the variation in the soil property of interest. We then computed the fertiliser requirement needed to minimise the expected loss associated with predictions. Here, the expected loss is defined as the difference in profit between applying fertiliser based on the estimated soil nutrient concentration accounting for uncertainty relative to the profit that would be gained from fertiliser application given the true soil nutrient concentration in known. We also accounted for data acquisition costs in computing the expected profit.Results showed that calibration sample size outweighed the effect of total sample size on the uncertainty associated with predictions. Equally, for the same calibration set size, there were large differences in the kriging variance between total sample sizes. When data acquisition costs were disregarded, the expected loss for available P was particularly affected by the total sample size. For available K, the calibration sample size had a predominant effect on the expected loss. The expected loss showed diminishing returns on investment suggesting that there is an optimum sample size. However, the expected profit was dominated by the costs of sampling and spectroscopy, indicating that currently using spectral methods to inform fertiliser management is not cost effective. That is, no combination of the total- and calibration sample sizes considered would result in a financial gain and could thus be considered optimal. Should costs substantially reduce then spectral methods offer a promising method for informing variable rate management. We conclude that the loss function approach is an appropriate method to assess whether soil spectroscopy is a cost-effective means to inform soil management. We further suggest its application in different case-studies to gain more robust insight in the value of applied soil spectroscopy.

The resilience of ecological systems is crucially important, particularly in the context of climate change. We present experimental evidence of critical slowing-down arising from perturbation of a key function in a complex ecosystem, exemplified by soil. Different behavioural classes in soil respiratory patterns were detected in response to repeated drying:rewetting cycles. We characterised these as adaptive, resilient, fragile or non-resilient. The latter involved increasing erratic behaviour (i.e. increasing variance), and the propagation of such behaviour (i.e. autocorrelation), interpreted as a critical slowing-down of the observed function. Soil microbial phenotype and land-use were predominantly related to variance and autocorrelation respectively. No relationship was found between biodiversity and resilience, but the ability of a community to be compositionally flexible rather than biodiversity per se appeared to be key to retaining system function. These data were used to map the extent to which soils are close to crossing into alternative stable states at a national scale.

Atmospheric variability in the North Atlantic region is known to modulate hydrometeorological variables across Europe. In this context, oscillatory systems, such as the NAO, may be used to indicate future water resource behaviours, such as hydrological droughts. Existing hydroclimate studies have identified a sensitivity of certain water resources to multiannual periodicities in systems such as the NAO and have highlighted that these long-term behaviours may be valuable to existing drought forecasting systems; for instance, by indicating multi-year periods of increased drought risk. However, the importance of multiannual NAO periodicities for driving water resource behaviour, and the feasibility of this relationship for indicating future droughts, has yet to be assessed in the context of known non-stationarities that are internal to the NAO and its influence on European meteorological processes. Here, we explore the role of NAO periodicities in defining water resource and drought behaviours over the past 90 years using a large dataset of 136 groundwater level records and 767 streamflow gauges in the UK. We identify significant relationships between the NAO and a calculated index of wide-spread water resource drought and find several abrupt shifts in drought frequency driven by non-stationarities in multiannual NAO behaviour. This includes a 7.5-year periodicity that has predominated water resource behaviour (and extremes) since the 1970s but has been weakening over recent years, suggesting a new shift in drought frequency may soon impact water resources. Furthermore, we show that the degree to which these periodicities have influenced recorded water resource anomalies is comparable to the projected effects of a worst-case climate change scenario. We discuss the potential origins for these modes of non-stationarity and their implications for existing water resource forecasting and projection systems, as well as the utility of these periodic behaviours as an indicator of future water resource drought in Europe.

Modern sensor technologies can provide detailed information about soil variation which allows for more precise application of fertiliser to minimise environmental harm imposed by agriculture. However, growers should lose neither income nor yield from associated uncertainties of predicted nutrient concentrations and thus one must acknowledge and account for uncertainties. A framework is presented that accounts for the uncertainty and determines the cost–benefit of data on available phosphorus (P) and potassium (K) in the soil determined from sensors. For four fields, the uncertainty associated with variation in soil P and K predicted from sensors was determined. Using published fertiliser dose–yield response curves for a horticultural crop the effect of estimation errors from sensor data on expected financial losses was quantified. The expected losses from optimal precise application were compared with the losses expected from uniform fertiliser application (equivalent to little or no knowledge on soil variation). The asymmetry of the loss function meant that underestimation of P and K generally led to greater losses than the losses from overestimation. This study shows that substantial financial gains can be obtained from sensor-based precise application of P and K fertiliser, with savings of up to £121 ha−1 for P and up to £81 ha−1 for K, with concurrent environmental benefits due to a reduction of 4–17 kg ha−1 applied P fertiliser when compared with uniform application. Biotechnology and Biological Sciences Research Council (BBSRC): BBS/E/C/000I0320; BBS/E/C/000I0330; BBS/E/C/000I0100.

Full list of authors: Morabito, L. K.; Jackson, N. J.; Mooney, S.; Sweijen, F.; Badole, S.; Kukreti, P.; Venkattu, D.; Groeneveld, C.; Kappes, A.; Bonnassieux, E.; Drabent, A.; Iacobelli, M.; Croston, J. H.; Best, P. N.; Bondi, M.; Callingham, J. R.; Conway, J. E.; Deller, A. T.; Hardcastle, M. J.; McKean, J. P.; Miley, G. K.; Moldon, J.; Röttgering, H. J. A.; Tasse, C.; Shimwell, T. W.; van Weeren, R. J.; Anderson, J. M.; Asgekar, A.; Avruch, I. M.; van Bemmel, I. M.; Bentum, M. J.; Bonafede, A.; Brouw, W. N.; Butcher, H. R.; Ciardi, B.; Corstanje, A.; Coolen, A.; Damstra, S.; de Gasperin, F.; Duscha, S.; Eislöffel, J.; Engels, D.; Falcke, H.; Garrett, M. A.; Griessmeier, J.; Gunst, A. W.; van Haarlem, M. P.; Hoeft, M.; van der Horst, A. J.; Jütte, E.; Kadler, M.; Koopmans, L. V. E.; Krankowski, A.; Mann, G.; Nelles, A.; Oonk, J. B. R.; Orru, E.; Paas, H.; Pandey, V. N.; Pizzo, R. F.; Pandey-Pommier, M.; Reich, W.; Rothkaehl, H.; Ruiter, M.; Schwarz, D. J.; Shulevski, A.; Soida, M.; Tagger, M.; Vocks, C.; Wijers, R. A. M. J.; Wijnholds, S. J.; Wucknitz, O.; Zarka, P.; Zucca, P., The International LOFAR Telescope is an interferometer with stations spread across Europe. With baselines of up to ~2000 km, LOFAR has the unique capability of achieving sub-arcsecond resolution at frequencies below 200 MHz. However, it is technically and logistically challenging to process LOFAR data at this resolution. To date only a handful of publications have exploited this capability. Here we present a calibration strategy that builds on previous high-resolution work with LOFAR. It is implemented in a pipeline using mostly dedicated LOFAR software tools and the same processing framework as the LOFAR Two-metre Sky Survey (LoTSS). We give an overview of the calibration strategy and discuss the special challenges inherent to enacting high-resolution imaging with LOFAR, and describe the pipeline, which is publicly available, in detail. We demonstrate the calibration strategy by using the pipeline on P205+55, a typical LoTSS pointing with an 8 h observation and 13 international stations. We perform in-field delay calibration, solution referencing to other calibrators in the field, self-calibration of these calibrators, and imaging of example directions of interest in the field. We find that for this specific field and these ionospheric conditions, dispersive delay solutions can be transferred between calibrators up to ~1.5° away, while phase solution transferral works well over ~1°. We also demonstrate a check of the astrometry and flux density scale with the in-field delay calibrator source. Imaging in 17 directions, we find the restoring beam is typically ~0.3′′ ×0.2′′ although this varies slightly over the entire 5 deg2 field of view. We find we can achieve ~80–300 μJy bm−1 image rms noise, which is dependent on the distance from the phase centre; typical values are ~90 μJy bm−1 for the 8 h observation with 48 MHz of bandwidth. Seventy percent of processed sources are detected, and from this we estimate that we should be able to image roughly 900 sources per LoTSS pointing. This equates to ~ 3 million sources in the northern sky, which LoTSS will entirely cover in the next several years. Future optimisation of the calibration strategy for efficient post-processing of LoTSS at high resolution makes this estimate a lower limit. © ESO 2022., This work made use of the Dutch national e-infrastructure with the support of the SURF Cooperative using grant no. EINF-262 LKM is grateful for support from the Medical Research Council (grant MR/T042842/1). S.M. acknowledges support from the Governmentof Ireland Postgraduate Scholarship Programme. E.B. acknowledges support from the ERC-ERG grant DRANOEL, n.714245. A.D. acknowledges support by the BMBF Verbundforschung under the grant 052020. J.H.C. acknowledges support from the UK Science and Technology Facilities Council (ST/R000794/1). P.N.B. is grateful for support from the UK STFC via grant ST/R000972/1. J.R.C. thanks the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) for support via the Talent Programme Veni grant. M.J.H. acknowledges support from the UK Science and Technology Facilities Council (ST/R000905/1). J.P.M. acknowledges support from the NetherlandsOrganization for Scientific Research (NWO, project number 629.001.023) and the Chinese Academy of Sciences (CAS, project number 114A11KYSB20170054). J.M. acknowledges financial support from the State Agency for Research of the Spanish MCIU through the “Center of Excellence Severo Ochoa” award to the Instituto de Astrofísicade Andalucía (SEV-2017-0709) and from the grant RTI2018-096228-B-C31 (MICIU/FEDER, EU). R.J.v.W. acknowledges support from the ERC Starting Grant ClusterWeb 804208. D.J.S. acknowledges support by the GermanFederal Ministry for Science and Research BMBF-Verbundforschungsprojekt D-LOFAR 2.0 (grant numbers 05A20PB1). LOFAR (van Haarlem et al. 2013) is the Low Frequency Array designed and constructed by ASTRON. It has observing, data processing, and data storage facilities in several countries, that are owned by various parties (each with their own funding sources), and that are collectively operated by the ILT foundation under a joint scientific policy. The ILT resources have benefitted from the following recent major funding sources: CNRS-INSU, Observatoire de Paris and Université d’Orléans, France; BMBF, MIWF-NRW, MPG, Germany; Science Foundation Ireland (SFI), Department of Business, Enterprise and Innovation (DBEI), Ireland; NWO, The Netherlands; The Science and Technology Facilities Council, UK; Ministry of Science and Higher Education, Poland.

We present an updated analysis of the mass composition of cosmic rays in the energy range of $10^{16.8}$ to $10^{18.3}$ eV. It is based on measurements with the LOFAR telescope of the depth of shower maximum, $X_{\rm max}$. We review the improvements to the simulation-based reconstruction setup, as well as the selection method to obtain a minimally biased $X_{\rm max}$ dataset. Results include estimates of the mean and standard deviation of the $X_{\rm max}$ distribution. A statistical analysis at distribution level has been done as well, using a four-component model of light to heavy nuclei. It confirms our previous results showing a significant low-mass fraction in this energy range. The radio technique has advanced enough that multiple observatories are publishing results on $X_{\rm max}$. As the array layouts and methods vary, it is interesting to compare the approaches, in light of the observed differences in the $X_{\rm max}$ results. We therefore show additional information on bias tests used in the $X_{\rm max}$ reconstruction and sample selection process.

Additional file 1. Contains Tables S1-S4 and Figures S1-S6, providing further information on data sources, model structures and results of sensitivity analyses.

The low flux of ultra-high-energy cosmic rays (UHECRs) makes it challenging to understand their origin and nature. A very large effective aperture is provided by the Lunar Askaryan technique. Particle cascades in a dielectric medium produce radio emission through the Askaryan effect. Ground based radio telescopes are used to search for nanosecond radio pulses that are produced when cosmic rays or neutrinos interact with the Moon’s surface. The LOw Frequency ARray (LOFAR) is currently the largest radio array operating at frequencies between 110−190 MHz; the optimum frequency range for Lunar signal search and 30−80 MHz for radio detection of air showers. One minute of observation has been carried out with six LOFAR stations beam-formed towards the Moon. In this contribution, we present some preliminary results of the analysis of the data and a complete description of the analysis steps.

The low frequency segment of SKA in Australia will have an extremely dense antenna array spanning an area of roughly 0.5 km$^2$. It offers unique possibilities for high‐resolution observations of air showers. Compared to LOFAR, it will have a much more homogeneous ground coverage, an increased frequency bandwidth (50-350 MHz), and the possibility to continuously observe with nearly 100% duty cycle. SKA will observe air showers in the range 10$^{16}$ eV - 10$^{18}$ eV with a reconstruction resolution on \xmax\ of around 10 g/cm$^2$. This allows for a high‐precision study of mass composition in the energy regime where a transition is expected from Galactic to extragalactic origin. In addition, SKA will be able to put constraints on hadronic interaction models, which is crucial for interpreting the data in this complex energy range. In this talk, we will show the results of a full detector simulation and demonstrate the capabilities of SKA, including energy and Xmax reconstruction, as well as more advanced methods to constrain the shape of the longitudinal development of air showers.

Full list of authors: Jackson, N.; Badole, S.; Morgan, J.; Chhetri, R.; Prūsis, K.; Nikolajevs, A.; Morabito, L.; Brentjens, M.; Sweijen, F.; Iacobelli, M.; Orrù, E.; Sluman, J.; Blaauw, R.; Mulder, H.; van Dijk, P.; Mooney, S.; Deller, A.; Moldon, J.; Callingham, J. R.; Harwood, J.; Hardcastle, M.; Heald, G.; Drabent, A.; McKean, J. P.; Asgekar, A.; Avruch, I. M.; Bentum, M. J.; Bonafede, A.; Brouw, W. N.; Brüggen, M.; Butcher, H. R.; Ciardi, B.; Coolen, A.; Corstanje, A.; Damstra, S.; Duscha, S.; Eislöffel, J.; Falcke, H.; Garrett, M.; de Gasperin, F.; Griessmeier, J. -M.; Gunst, A. W.; van Haarlem, M. P.; Hoeft, M.; van der Horst, A. J.; Jütte, E.; Koopmans, L. V. E.; Krankowski, A.; Maat, P.; Mann, G.; Miley, G. K.; Nelles, A.; Norden, M.; Paas, M.; Pandey, V. N.; Pandey-Pommier, M.; Pizzo, R. F.; Reich, W.; Rothkaehl, H.; Rowlinson, A.; Ruiter, M.; Shulevski, A.; Schwarz, D. J.; Smirnov, O.; Tagger, M.; Vocks, C.; van Weeren, R. J.; Wijers, R.; Wucknitz, O.; Zarka, P.; Zensus, J. A.; Zucca, P., The Low-Frequency Array (LOFAR) Long-Baseline Calibrator Survey (LBCS) was conducted between 2014 and 2019 in order to obtain a set of suitable calibrators for the LOFAR array. In this paper, we present the complete survey, building on the preliminary analysis published in 2016 which covered approximately half the survey area. The final catalogue consists of 30 006 observations of 24 713 sources in the northern sky, selected for a combination of high low-frequency radio flux density and flat spectral index using existing surveys (WENSS, NVSS, VLSS, and MSSS). Approximately one calibrator per square degree, suitable for calibration of ≥200 km baselines is identified by the detection of compact flux density, for declinations north of 30° and away from the Galactic plane, with a considerably lower density south of this point due to relative difficulty in selecting flat-spectrum candidate sources in this area of the sky. The catalogue contains indicators of degree of correlated flux on baselines between the Dutch core and each of the international stations, involving a maximum baseline length of nearly 2000 km, for all of the observations. Use of the VLBA calibrator list, together with statistical arguments by comparison with flux densities from lower-resolution catalogues, allow us to establish a rough flux density scale for the LBCS observations, so that LBCS statistics can be used to estimate compact flux densities on scales between 300 mas and 2′′, for sources observed in the survey. The survey is used to estimate the phase coherence time of the ionosphere for the LOFAR international baselines, with median phase coherence times of about 2 min varying by a few tens of percent between theshortest and longest baselines. The LBCS can be used to assess the structures of point sources in lower-resolution surveys, with significant reductions in the degree of coherence in these sources on scales between 2′′ and 300 mas. The LBCS survey sources show a greater incidence of compact flux density in quasars than in radio galaxies, consistent with unified schemes of radio sources. Comparison with samples of sources from interplanetary scintillation (IPS) studies with the Murchison Widefield Array shows consistent patterns of detection of compact structure in sources observed both interferometrically with LOFAR and using IPS. © ESO 2022., Support for the operation of the MWA is provided by the Australian Government (NCRIS), under a contract to Curtin University administered by Astronomy Australia Limited. We acknowledge the Pawsey Supercomputing Centre which is supported by the Western Australian and Australian Governments. A.D. acknowledges support by the BMBF Verbundforschung under the grant 052020. L.K.M. is grateful for support from the UKRI Future Leaders Fellowship (grant MR/T042842/1). J. Moldón acknowledges financial support from the State Agency for Research of the Spanish MCIU through the “Center of Excellence Severo Ochoa” award to the Instituto de Astrofísica de Andalucía (SEV-2017-0709) and from the grant RTI2018-096228-B-C31 (MICIU/FEDER, EU). J.P.M. acknowledges support from the Netherlands Organization for Scientific Research (NWO, project number 629.001.023) and the Chinese Academy of Sciences (CAS, project number 114A11KYSB20170054).

Ecological restoration has a paradigm of re-establishing ‘indigenous reference' communities. One resulting concern is that focussing on target communities may not necessarily create systems which function at a high level or are resilient in the face of ongoing global change. Ecological complexity – defined here, based on theory, as the number of components in a system and the number of connections among them – provides a complementary aim, which can be measured directly and has several advantages. Ecological complexity encompasses key ecosystem variables including structural heterogeneity, trophic interactions and functional diversity. Ecological complexity can also be assessed at the landscape scale, with metrics including β diversity, heterogeneity among habitat patches and connectivity. Thus, complexity applies, and can be measured, at multiple scales. Importantly, complexity is linked to system emergent properties, e.g. ecosystem functions and resilience, and there is evidence that both are enhanced by complexity. We suggest that restoration ecology should consider a new paradigm to restore complexity at multiple scales, in particular of individual ecosystems and across landscapes. A complexity approach can make use of certain current restoration methods but also encompass newer concepts such as rewilding. Indeed, a complexity goal might in many cases best be achieved by interventionist restoration methods. Incorporating complexity into restoration policies could be quite straightforward. Related aims such as enhancing ecosystem services and ecological resilience are to the fore in initiatives such as the Sustainable Development Goals and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. Implementation in policy and practice will need the development of complexity metrics that can be applied at both local and regional scales. Ultimately, the adoption of an ecological complexity paradigm will be based on an acceptance that the ongoing and unprecedented global environmental change requires new ways of doing restoration that is fit for the future.

Drought forecasting and early warning systems for water resource extremes are increasingly important tools in water resource management, particularly in Europe where increased population density and climate change are expected to place greater pressures on water supply. In this context, the North Atlantic Oscillation (NAO) ais often used to indicate future water resource behaviours (including droughts) over Europe, given its dominant control on winter rainfall totals in the North Atlantic region. Recent hydroclimate research has focused on the role of multiannual periodicities in the NAO in driving low frequency behaviours in some water resources, suggesting that notable improvements to lead-times in forecasting may be possible by incorporating these multiannual relationships. However, the importance of multiannual NAO periodicities for driving water resource behaviour, and the feasibility of this relationship for indicating future droughts, has yet to be assessed in the context of known non-stationarities that are internal to the NAO and its influence on European meteorological processes. Here we quantify the time-frequency relationship between the NAO and a large dataset of water resources records to identify key non-stationarities that have dominated multiannual behaviour of water resource extremes over recent decades. The most dominant of these is a 7.5-year periodicity in water resource extremes since approximately 1970 but which has been diminishing since 2005. Furthermore, we show that the non-stationary relationship between the NAO and European rainfall is clearly expressed at multiannual periodicities in the water resource records assessed. These multiannual behaviours are found to have modulated historical water resource anomalies to an extent that is comparable to the projected effects of a worst-case climate change scenario. Furthermore, there is limited systematic understanding in existing atmospheric research for non-stationaries in these periodic behaviours which poses considerable implications to existing water resource forecasting and projection systems, as well as the use of these periodic behaviours as an indicator of future water resource drought.

Globally, rapid urban expansion has caused green spaces in urban areas to decline considerably. In this study, the rapid expansion of three Southeast Asia cities were considered, namely, Kuala Lumpur City, Malaysia, Jakarta, Indonesia, and Metro Manila, Philippines. This study evaluates the changes in spatial and temporal patterns of urban areas and green space structure in the three cities over the last two decades. Land use land cover (LULC) maps of the cities (1988/1989, 1999 and 2014) were developed based on 30-m resolution satellite images. The changes in the landscape and spatial structure were analysed using change detection, landscape metrics and statistical analysis. The percentage of green space in the three cities reduced in size from 45% to 20% with the rapid expansion of urban areas over the 25-year period. In Metro Manila and Jakarta, the proportion of green space converted to urban areas was higher in the initial 1989 to 1999 period than over the latter 1999 to 2014 period. Significant changes in green space structure were observed in Jakarta and Metro Manila. Green space gradually fragmented and became less connected and more unevenly distributed. These changes were not seen in Kuala Lumpur City. Overall, the impact of spatial structure of urban areas and population density on green space is higher in Jakarta and Metro Manila when this is compared to Kuala Lumpur. Thus, the results have the potential to clarify the relative contribution of green space structure especially for cities in Southeast Asia where only a few studies in urban areas have taken place.

Ensuring ecosystem resilience is an intuitive approach to safeguard the functioning of ecosystems and hence the future provisioning of ecosystem services (ES). However, resilience is a multi-faceted concept that is difficult to operationalize. Focusing on resilience mechanisms, such as diversity, network architectures or adaptive capacity, has recently been suggested as means to operationalize resilience. Still, the focus on mechanisms is not specific enough. We suggest a conceptual framework, resilience trinity, to facilitate management based on resilience mechanisms in three distinctive decision contexts and time-horizons: i) reactive, when there is an imminent threat to ES resilience and a high pressure to act, ii) adjustive, when the threat is known in general but there is still time to adapt management, and iii) provident, when time horizons are very long and the nature of the threats is uncertain, leading to a low willingness to act. Resilience has different interpretations and implications at these different time horizons, which also prevail in different disciplines. Social ecology, ecology, and engineering are often implicitly focussing on provident, adjustive, or reactive resilience, respectively, but these different notions and of resilience and their corresponding social, ecological, and economic trade-offs need to be reconciled. Otherwise, we keep risking unintended consequences of reactive actions, or shying away from provident action because of uncertainties that cannot be reduced. The suggested trinity of time horizons and their decision contexts could help ensuring that longer-term management actions are not missed while urgent threats to ES are given priority.