Ecology

Spotted wing drosophila captures within the United StatesComparison between the raspberry field and wooded area, during pre-harvest and harvest periods to account for presence of developing and fully ripened fruit, SWD captures and selectivity per QB dry sticky trap is found in Fig. 1A,B. No difference was found in average capture per trap between either area during the pre-harvest period, nor was there a difference between these and the field during the harvest period. The wooded area during the harvest period captured the greatest amount of SWD/trap (F1,209 = 7.335, P = 0.007) (Fig. 1A). Dry sticky traps baited with QB had a significantly higher selectivity during the pre-harvest period in the raspberry field than in the wooded area but was not significantly different from the trap selectivity in the wooded area during the harvest period. The pre-harvest wooded area trap selectivity was not different from the harvest field trap selectivity. While the harvest field trap selectivity was lower than that of the wooded area trap selectivity during the same period (F1,203 = 23.6, P  More

1.Root, T. L. et al. Nature 421, 57–60 (2003).CAS 
Article 

Google Scholar 
2.Morelli, T. L. et al. Front. Ecol. Environ. 18, 228–234 (2020).Article 

Google Scholar 
3.Armstrong, J. B. et al. Nat. Clim. Change https://doi.org/10.1038/s41558-021-00994-y (2021).4.Northcote, T. G. N. Am. J. Fish. Manage. 17, 1029–1045 (1997).Article 

Google Scholar 
5.Xu, C., Letcher, B. H. & Nislow, K. H. Freshwater Biol. 55, 2253–2264 (2010).Article 

Google Scholar 
6.Schlosser, I. J. BioScience 41, 704–712 (1991).Article 

Google Scholar 
7.Al-Chokhachy, R., Alder, J., Hostetler, S., Gresswell, R. & Shepard, B. Glob. Change Biol. 19, 3069–3081 (2013).Article 

Google Scholar 
8.Fausch, K. D., Torgersen, C. E., Baxter, C. V. & Li, H. W. BioScience 52, 483–498 (2002).Article 

Google Scholar 
9.Muhlfeld, C. C. et al. Science 360, 866–867 (2018).CAS 

Google Scholar 
10.Kovach, R. P. et al. Rev. Fish Biol. Fisher. 26, 135–151 (2016).Article 

Google Scholar 
11.Isaak, D. J., Young, M. K., Nagel, D. E., Horan, D. L. & Groce, M. C. Glob. Change Biol. 21, 2540–2553 (2015).Article 

Google Scholar 
12.Isaak, D. J. et al. Water Resour. Res. 53, 9181–9205 (2017).Article 

Google Scholar 
13.Small-Lorenz, S. L., Culp, L. A., Ryder, T. B., Will, T. C. & Marra, P. P. Nat. Clim. Change 3, 91–93 (2013).Article 

Google Scholar 
14.Rieman, B. E. & Dunham, J. B. Ecol. Freshw. Fish 9, 51–64 (2000).Article 

Google Scholar 
15.Guzzo, M. M., Blanchfield, P. J. & Rennie, M. D. Proc. Natl Acad. Sci. USA 114, 9912–9917 (2017).CAS 
Article 

Google Scholar 
16.Brennan, S. R. et al. Science 364, 783–786 (2019).CAS 
Article 

Google Scholar 
17.Hauer, F. R. et al. Sci. Adv. 2, e1600026 (2016).Article 

Google Scholar  More

Traits are attributes that speak to biophysical limitations, pressure on species, ecological functionality, and interactions. They have found their way to the forefront of many discussions and debates about ecosystem dynamics and, with a slight time lag, social-ecological systems1,2,3. The promise is that a traits framework can further our understanding of patterns, dynamics, interactions, and tipping points within and across complex social-ecological systems. But what will it take to make good on this promise, in particular for our cities, where change is fast and—being the places where the majority of humans live—human perceptions are particularly diverse? What kind of framing, what research, would allow traits—classically understood as a different representation and interpretation of well-established and known properties of the social-ecological system―to fully work as “mediators” for understanding the behavior, functions, and needs of urban systems under pressure?This perspective aims to contribute to the current wide-ranging discussion about traits in both theoretical and applied ecology, and parallel work on better understanding human connections to nature. To this end, we explore the potential of using an expanded conceptualization of traits as a platform for integrated approaches to understanding the different facets of people-in-nature relationships and dynamics4,5.Expanding from the original “characteristics which have demonstrable links to the organism’s function”6, we see traits as a nexus where different theories and conceptualisations about social-ecological systems can connect, intertwine and comprehensively allow us to assess the current state of a system—and even more importantly, evaluate the implications of change (Box 1 and Fig. 1). To make it an integrative and useful framework for urban studies and policy/practice, traits need to be easy to recognise and relevant to decision makers across scales and in different contexts. In addition, information on trait profiles—generic as well as site specific—need to be easily available through monitoring or in databases.Fig. 1: Traits within social ecological systems.Theoretical flow chart linking the entities of a social-ecological system to its traits, demonstrating how a traits framework—as outlined in this article—might be positioned to support the analysis, interpretation and governance of urban systems.Full size imageOur argument is threefold: The first dimension focuses on how to assess and anticipate change by establishing chains of interconnected traits that describe and causally connect sensitivity and response to different urban pressures such as heat, soil compaction, environmental toxicants, and stormwater runoff, understood through “response” traits7,8,9,10 to their functional consequences11, mediated by “effect traits”. The second dimension is grounded in human perceptions and appraisal of diversity to highlight the different cues and characteristics people use to detect change or articulate value narratives, and it is linked to the role of traits in ecological literacy. Here, we propose traits be viewed as boundary objects, i.e., features that carry meaning across society (although the meanings might be diverse and sometimes conflicting), and that this second dimension is essential for understanding the role of society and humans in a traits framework. The third dimension outlines how the first two dimensions connect to inform and support decision making and management at different scales, for example in different, multilayer, and multiactor governance processes12 (Fig. 2).Fig. 2: A traits framework for scientific study and practical application.The three dimensions of a social-ecological traits framework for understanding and governing urban systems. The first dimension is represented by observable traits of the urban environment, e.g., features of humans and other co-inhabiting species and their differing responses to pressures and selection, leading to functional consequences and finally, altered characters of an urban social-ecological system. The second dimension is characterized by feedback loops between those effect outcomes and individual and collective perceptions and decision making. Lastly, the third dimension is represented by urban ecosystem planning and management embedded in governance processes and instruments. Through its ability to connect different spheres and discourses, an expanded traits framework can aim for effective and inclusive decision support that is responsive and place-adapted. By expanding and bridging these three dimensions, we can connect different insights and knowledge about ecosystem function and human perceptions, values and interactions with the environment. This will support the development of a (meta-) theoretically grounded, practically applicable traits framework to interrogate reciprocal feedback linkages and nature-human relationships. The figure includes resources from Freepik.com.Full size imageBox 1 definitionsFunctional trait: A feature of an organism which has demonstrable links to the organism’s function69, and, as such, “determines the organism’s response to pressures (response trait), and/or its effects on ecosystem processes or services (effect trait). In plants, functional traits include morphological, ecophysiological, biochemical and regeneration traits, including demographic traits (at population level). In animals, these traits are combined with life history and behavioral traits (e.g., guilds, organisms that use similar resources/habitats)”70, p. 2779.Boundary object: “[…] those […] objects which both inhabit several intersecting social worlds and satisfy the information requirements of each of them. Boundary objects are objects, which are both plastic enough to adapt to local needs and the constraints of the several parties involving them, yet robust enough to maintain a common identity across sites. […] They have different meanings in different social worlds [and across cultures] but their structure is common enough to more than one world to make them recognizable, a means of translation.”71 p. 393, see also72.Social-ecological traits (expanded definition): An ecologically or socially (inter)active and demonstrable feature of the environment at any level or scale. A social-ecological trait either mediates reactions to selective social-ecological filtering (response trait) or determines effects on ecosystem processes or services (effect trait), or both. The aggregate trait profile of a given entity should ideally speak both to ecological functioning and socio-cultural meaning.The first dimension: response and its effect outcomesTrait-based approaches have been used for descriptive purposes13 to enable broader global comparisons that transcend the constraints of regional taxonomic diversity (e.g., see refs. 6,14) and allow for the types of generalizations sought in ecology15,16. Traits offer a way of looking at causality and change, and trait profiles can indicate whether emergent communities are functionally different from historic communities. To this end, traits can be divided into those that determine an organism’s sensitivity and response to environmental factors, and those that relate to its effect on the environment4,17. When combined, the two categories of traits can be used to detect, identify and monitor the current state of ecosystems, and to anticipate the outcomes of change8,10,17,18,19.An environment described through traits: The urban bio-physical environment includes hydrology and soils, as well as biotic elements (flora and fauna), and understanding the relationships among those components is necessary to measure and anticipate the profound effects of urbanisation. Currently, knowledge of plant traits is most developed4,20, although there is work emerging on traits for animals or other taxonomic groups8,21 as well as for soil and geodiversity22. Animal studies so far tend to focus on habitat modelling for birds, insects, invertebrates and a few on mammals (e.g., see refs. 3,8,16,23). Many studies have looked at the impact of different community assemblages on ecological functions through effect traits and, in particular, how altered or dynamically changing communities will affect ecosystem process through changes in representation of effect traits (but e.g., see ref. 23). However, the link between traits and ecosystem functions has largely been inferred (ibid.), and is, according to Cadotte et al.24, rudimentary (see also25 and26). As we indicated with our definition of traits (Box 1), we see a value in including soil properties as traits and not to leave them as “environmental filters”, as this may offer a more dynamic way of understanding one of the major urban processes of change—soil sealing and compaction—and thus help guide urban development.Traits at different levels and scales: Traits at the species level are by far the best known and most explored, but there are also studies that use traits from other ecological levels—gene, community, ecosystem and landscape—as indicators for tracking response to stress27 and calculating functional “performance”. A common approach to scale is to aggregate species level information. For example, the average values of aggregations of plant species traits at the ecosystem level provide a basis for calculating overall sensitivity to pressures28. This in turn, and drawing on different sets of traits, allows for estimations of changes in ecosystem function (e.g., see ref. 29). However, there are other characteristics that could also be understood as traits. At the landscape level the mosaic of ecosystems and the location and combination of patches are used to assess flows and exchange across larger areas (e.g., see ref. 30). A good example is a city in a river valley, where water flows and exact location within the drainage basin affect urban green spaces and their aggregated matter production, CO2 absorption or carbon sub-section31. Aggregate, or higher-level traits, such as structural composition and functional diversity of vegetation, matter flows, or species migration, are the most common traits analysed through remote sensing in order to track trends25. More work needs to be done to explore relevant traits at different levels of organisation to match the scale and nature of disturbances and the spatial and temporal scale at which different functions are most relevant. Being explicit about scale, and ensuring traits at different levels are nested, allows for tracking of processes across scales.Individual traits, trait combinations, and interlinked suites of traits: A key promise of traits is to provide mechanistic explanations of observed structure, patterns and functionality, which is usually demonstrated through statistical correlations. Further developing suites of response and effect traits could provide valuable input and indicators for assessment and monitoring frameworks. For example, traits could inform DPSIR (drivers, pressures, state, impact, and response) models by anticipating or measuring response to a pressure and the direct and indirect impact this response could have. At a more fundamental level, traits explain whether impacts may be causing a change in the functional state of the system. Interlinked traits, from those determining sensitivity, to those mediating response elicited by sensitivity, could improve mechanistic understanding by supporting the development of stepwise response-effect pathways17. For example, land conversion—like the soil sealing and compaction typical in cities—fundamentally alters soil properties, which in turn affects vegetation. Soil properties influence the growth and composition of plant communities. This translates into trait-mediated effects like reduction of total leaf area, which leads to cascading effects of early leaf senescence and limitation of stomatal transpiration. This reduces water exchange capacity, which in turn is key for mediating air cooling or shading and other functions/services plants may offer to humans.For this first dimension, trait databases, classical field inventories, and experiments, remote sensing data, and GIS-based information are crucial15,32. We see valuable developments from the past two decades of research towards achieving a traits response-effect library in both the ecology and remote sensing communities33,34, even if recent advances from remote sensing studies still rarely find entrance into urban planners’ work and policy decision-making35. In particular, the development in the technical dimensions of detecting traits and trait variation20,34, and tracking these over time, has recently rapidly developed. The progress in application of high-resolution hyperspectral data, light detection, and ranging (LiDAR) or the possibility of mounting the recently developed sensors on unmanned aerial vehicles (UAVs) equip the researchers with addditional tools that can not only expand the range of measurable traits but also allow easy access to data. This provides a powerful support for urban planning and, ultimately, urban governance. Moreover, applications for tablets or smartphones offer alternative ways to directly involve citizens in ecosystem monitoring and further develop citizen science (e.g., see refs. 22,36).The second dimension: traits as an interdisciplinary bridgeThe literature explicitly using the term traits tends to focus on soil, geodiversity, plant, and community trait profiles as an outcome of social-ecological selection through environmental conditions, species interactions, human preferences, management regimes etc. (e.g., see refs. 4,37). This approach has started to address not just how people filter traits (e.g., see ref. 38), but the reason(s) behind either individual or group decisions that lead to filtering (e.g., see refs. 39,40). Here, we propose that the environment, described through traits, could be considered a boundary object (Box 1), allowing for a multiplicity of views, disciplinary connections, engagements, and perceptions, and that speaks to the complexity of social-ecological systems. This will expand the range of functions used to describe a system, and the types of traits required to capture them.Ecological functions relative to ecosystem services: The plant and animal traits that people respond to may not be the same ones that mediate responses to environmental change. For example, seed mass and specific leaf area are important plant functional traits41 but are less likely to influence people’s preferences for urban vegetation (e.g., see ref. 42). Indeed, some esthetic traits promoted by human decision-making and management, such as selection for leaf variation and predominantly deciduous plants, may also lead to the predominance of woody plants that are strongly affected by water stress, fungal attack or insect infestation or trimmed canopies, and thus promote reduced fitness of individual organisms and communities43. On the other hand, a successful reproductive strategy such as the emission of high quantities of pollen might limit the suitability to human-dominated environments (including cities) due to allergenic potential44. Do we need more, or different traits to link ecosystem dynamics more strongly to the lived reality of people? Are traits too simplistic proxies, or perhaps too specific features, to express and understand people–nature interactions? Introducing humans and human appraisal into our trait framework encourages a broader definition of what might be relevant traits. Traits used in this way provide a specific link to interactions and feedback mechanisms between human wellbeing and functional ecology (and respective proxies that serve multiple relational (feedback) purposes).Traits as relational features: Trait lists already include features which are easy to understand and readily detected by human sensory organs, and thus find traction in society or connect to existing ethno-biological narrations39. Traits such as flower colour, leaf shape, and canopy density, which may not necessarily be considered central functional traits, are important drivers of people’s preferences37,39,45,46. Both size and colour of the flowers are plant traits affecting people’s perception47 and can thus be an important factor for gaining societal approval for more urban greenery48. Seasonality is another relevant trait; for example, an extended flowering season49. At the same time, there is a growing interest in flowers and blooming meadows among gardeners worldwide also to support insects in urban landscapes to counteract global biodiversity decline37,39.In this vein, we argue that traits are a formative force influencing human wellbeing and world views, giving shape to ecological systems and linked human affordances (through, e.g., shade and sensory stimuli), and social systems by shaping the context of human activities and experiences. For example, we know that people recognize and value a wide range of plant traits, and that this has even been identified as a useful way to speak about the state of nature and large scale change50. There is evidently a role for traits and trait composition as language for more “functional” ecological literacy36,50. This position as a boundary object needs to be further explored and linked to the responses of social-ecological urban systems, which are subject to a multitude of pressures, including climate change and soil sealing.Traits as boundary objects and connectors between knowledge systems: What is needed to better position and connect the concept of traits to multiple different literatures and disciplines and enable traits to be used as a useful boundary object? Many disciplines outside the ecological and environmental sciences have an interest in understanding ecosystem function and biodiversity, and how people relate to these ideas. Traits, and deeper meanings of some traits, can be found within environmental psychology, ethno-botany/zoology and environmental anthropology. Trait-based approaches may also be well suited to engage with other ways of knowing, such as traditional ecological knowledge and religious systems. This disciplinary and trans-disciplinary knowledge is needed if traits are to connect social-ecological attributes to diverse human values and wellbeing dimensions, and to ensure we do not produce trivial and culturally biased conclusions51,52. Based on the diverse use and potential meanings of the word “traits”, we argue that a traits framework, and traits-focused interdisciplinary discussions and projects, could support a dual ontological stance where some connections are more universal, while others are inherently interpretational or simply individual. Hence, this may help to effectively connect the social and cultural dimensions of traits to a deep ecological understanding of change and its multiple consequences. This would be an important development that allows for critical engagement with concepts like tipping points and system states and what they actually mean in a complex social–ecological urban system.The third dimension: traits for decision supportThe major purpose of the traits concept, as we present it here, is to develop an ontologically inclusive traits framework capable of addressing both the resilience of ecological functions and the experiential and relational aspects of human interactions with nature. On the applied side, this would be relevant to a wide range of decision-making processes, not least urban planning. Clearly visible and easy-to-map traits are well-suited as indicators to describe the state of urban landscapes relevant for biodiversity and society alike. To this end, there are still many questions that need answers. For example, how can the understanding of trait profiles help improve species selection in times of climate change, to inform management priorities and strengthen cross-community stewardship, especially where the diversity of response traits may be low? And which traits are incompatible and how are they best kept separate, a question particularly relevant in the light of zoonosis like the COVID-19 pandemic in 2020? And finally, what traits could best serve as reasonable proxies or indicators to provide either cues or early signals of species responses to (fundamental) change in urban environments?Supporting holistic decisions: Already now we see increasing use of traits in modelling and decision support tools like CiTree and iTree53,54. As cities strive to adapt to climate change by, for example, revising tree species selection (e.g., see ref. 55), an improved understanding of the relationship between detectable functional traits and the provision of ecosystem services can help avoid maladaptation56. For example, replacing shade trees with fine-foliaged trees may improve adaptation to future climates but would not provide the same levels of climate mitigation57. From a decision-making point of view, key traits are those determining the response of ecosystems to human-induced pressures such as air pollution, soil sealing, or urban heat islands, as well as those mediating the effects of these changes on ecosystem services and related benefits as perceived by people8,58.A traits framework that uses our social-ecological definition of traits might support informed decisions on trade-offs. For example, invasive or non-native plants are often seen as ecologically problematic, but certain traits such as high leaf coverage or flower colour and shape make them socially desirable48. Traits connected to more social-ecological dimensions will allow for a more holistic assessment of options and the potential trade-off implications of different choices. While decisions are often grounded, implicitly or explicitly, in considerations of multiple traits (e.g., see ref. 53), we need to ensure that traits considered in the plant selection include both traits related to broad and diverse preferences and desires for ecosystem services and traits, that ensure a resilient response to drivers of change that may impact their ability to provide these services (see, e.g., the scoring system for urban vegetation species proposed by Tiwary et al.59).Urban planning informed by an expanded traits framework and spatial-temporal patterns of trait profiles has the promise to be adaptive in the best sense and thus, resilient. More city and regional comparisons are needed to make target setting and threshold discussions grounded and allow for global discussion. This requires a targeted effort at broader inclusion of cases and trait data from different climates, biomes, multiple ecological levels but also cultures, and would move traits studies towards a truly transdisciplinary venture with real impact on how we plan and manage our cities.Feasible and easy to use: Indicator traits need to be robust, easy to measure and low-cost to assess, and have a causal link to relevant social-ecological processes and patterns (such as ecosystem services for recreation, cooling or food4,60). The potential use in planning and decision-making at multiple levels again point to the need to discuss the scales and levels for traits studies to make sure trait levels are nested and logically commensurable. Higher-level, larger-scale properties such as landscape morphology and water availability, the profile of pest communities or potential invasions can be further informed by the development of more detailed traits frameworks. This makes traits frameworks highly relevant also from an economic, social and health perspective, especially in intensely managed environments like cities, where combinations of multiple stressors and external factors create small scale heterogeneity and fast temporal change in pressures61,62.Trait selection can play that important role for assisting in the planning and design and then evaluation of the functionality of high-biodiversity green spaces63, and for trait-informed assessment of “performance”, e.g., of ecologically protected areas. A relevant example to this point is the ongoing debate about how to evaluate ex-ante, and then monitor, the implementation of nature-based solutions62,64, which remains a challenge65. Could this be done using traits instead of commonly used area-based indicators? Could traits become the basis to design and assess the impacts of offsets and compensation measures, thus increasing their efficacy? From this perspective, we see in a traits framework the potential to support a shift towards more flexible and effective planning approaches, more suitable to address today’s urban challenges and to promote greater well-being, sustainability and resilience of present and future cities.Conclusion and looking aheadThrough their direct relation to ecosystem services such as cooling and fresh air, easy-to-understand traits can be an entry-point for nature awareness and, subsequently, ecological knowledge in decision-making both at the citizen and the societal level66. However, to make traits successful indicators of global, regional, or local environmental changes, it is vital that urban society is understood as diverse across characteristics such as cultural background, physical mobility, gender, age, degree of formal or informal education, access to information and communication, purchasing power, and political influence67. All these factors affect the needs, preferences, and values of individuals and groups, and the way each interpret human-nature relationships. Only by taking these factors into account, planning for spatial-temporal diversity in traits across an urban landscape will create more inclusive urban systems that foster multiple benefits for both people and biodiversity68.The expansion and implementation of a traits-based approach for urban systems is impeded by availability of traits data. For example, trait databases are usually a primary data source in studies on urban ecology, however, these data have mainly been collected in natural areas or controlled environments such as laboratories, where organisms may display different trait values than those in urban environments. Studies have also been concentrated in the global north, and there are major challenges with potentially transferring and adapting thinking mostly developed in the Global North to rapidly urbanising areas in Africa, Asia and South America.To enable a social-ecological traits framework for interdisciplinary discussion and for guiding urban planning and decision making, we suggest a three-pronged approach for building a social-ecological understanding of trait mediated interactions and their implications, and make this understanding useful to practice (Table 1). Large-scale monitoring needs to be coupled with in-depth understanding of response mechanisms and their impact on ecosystem functions as well as services, and a deeper connection between traits and human perception as well as sense-making of the world we live in. Application to human perception and sense-making requires more data, theory and empirical work, and especially the way people relate to traits will likely vary considerably across cities and contexts across the globe. All branches of investigation need to be embedded in an interdisciplinary discussion about the role that traits play for social-ecological interactions and mutual exchange. Drawing on this broad evidence base, synthesized knowledge will offer a more comprehensive support for urban decision making, not least in anticipation of future change.Table 1 Research agenda.Full size table More

Bioreactor operation and samplingA continuous-flow MBR made from Plexiglass with a working volume of 12 L was used for enrichment (Supplementary Fig. S1). The reactor was installed with a submerged hollow fiber ultrafiltration membrane module (0.02 μm pore size, Litree, China) with a total membrane surface area of 0.03 m2. A level control system was set up to prevent liquid overflowing. The reactor was fed with diluted real urine with Total Kjeldahl Nitrogen (TKN) concentration of 140–405 mg N L−1 (for detailed influent composition see Supplementary Table S1). Initially, the reactor was inoculated with activated sludge taken from the aeration tank of a municipal wastewater treatment plant (Tsinghua Campus Water Reuse). The pH was maintained at 6.0 ± 0.1 by adding 1 M NaOH to buffer acidification by ammonia oxidation. The airflow was controlled at 2 L min−1, leading to the dissolved oxygen (DO) concentration above 4 mg O2 L−1 as regularly measured by a DO probe (WTW Multi 3420). The airflow also served to wash the membrane and mix the liquid. The temperature was controlled at 22–25 °C. The initial hydraulic retention time (HRT) was 3 days and was decreased to 1.5 days on day 222. The sludge retention time (SRT) was infinite as no biomass was discharged.The MBR was operated for 490 days. During this period, influent and effluent samples (10 mL each) were collected 1–3 times per week and used to determine the concentrations of TKN, total nitrite nitrogen (TNN = NO2−-N + HNO2-N), and nitrate nitrogen, according to standard methods.19 Mixed liquid samples (25 mL) were also taken weekly to measure mixed-liquor suspended solids (MLSS) and mixed-liquor volatile suspended solids (MLVSS).19 Biomass samples (10 mL) were regularly taken for qPCR and microbial community analyses (see below).Batch testsIn order to test urea hydrolysis and subsequent nitrification in the enrichment culture, short-term incubations were performed in a cylindrical batch reactor (8 ×18.5 cm [d × h], made from Plexiglass). 150 mL biomass was sampled from the reactor and washed three times in 1 x PBS buffer to remove any remaining nitrogen source. Subsequently, the biomass was resuspended in a 400 mL growth medium, which contained urea (about 40 mg N L−1), NaHCO3 (120 mg L−1), and 2 mL Hunter’s trace elements stock. Dissolved oxygen was controlled above 4 mg O2 L−1. Biotic and abiotic controls were performed under identical conditions with NH4Cl (~40 mg N L−1) instead of urea. The pH in all batch assays was maintained at 6.0 ± 0.1 by adding 1 M HCl or NaOH. According to the microbial activities during long-term operation, each batch assay lasted 6 to 8 h, and samples (5 mL) were taken every 20 to 60 min. Biomass was removed by sterile syringe filter (0.45 μm pore size, JINTENG, China), and urea, ammonium, nitrite, and nitrate concentrations were determined as described above. All experiments were performed in triplicate.DNA extractionBiomass (2 mL) for DNA extraction was collected on days 0, 53, 98, 131, 161, 189, 210, 238, 266, 301, 321, 358, 378, 449, and 471. DNA was extracted using the FastDNA™ SPIN Kit for Soil (MP Biomedicals, CA, U.S.) according to the manufacturer’s protocols. DNA purity and concentration were examined using agarose gel electrophoresis and spectrophotometrically on a NanoDrop 2000 (ThermoFisher Scientific, Waltham, MA, USA).16S rRNA gene amplicon sequencing and data analysisThe V4-V5 region of the 16 S rRNA gene was amplified using the universal primers 515F (5′-barcode-GTGCCAGCMGCCGCGG-3′) and 907 R (5′-CCGTCAATTCMTTTRAGTTT-3′).20 PCR products were purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to manufacturer’s instructions and quantified using the QuantiFluor™ -ST (Promega, USA). Amplicons were pooled in equimolar concentrations and sequenced using the Illumina MiSeq PE3000 sequencer as per the manufacturer’s protocol. Amplicon sequences were demultiplexed and quality filtered using QIIME (version 1.9.1).21 Reads 10 bp were assembled. UPARSE (version 7.0.1090 http://drive5.com/uparse/) was used to cluster operational units (OTUs) on a 97% similarity cut-off level, and UCHIME to identify and remove chimeric sequences. The taxonomy of each 16S rRNA gene sequence was assigned by the RDP Classifier algorithm (http://rdp.cme.msu.edu/) according to the SILVA (SSU132) 16S rRNA database using a confidence threshold of 70%.Quantification of various amoA by qPCRTo quantify the abundances of comammox Nitrospira, AOB and AOA in the bioreactor, qPCR targeting the functional marker gene amoA was performed on DNA extracted from the bioreactor at different time points. We used the specific primers Ntsp-amoA 162F/359R amplifying comammox Nitrospira clades A and clade B simultaneously,12 Arch-amoAF/amoAR targeting AOA amoA,22 and amoA-1F/amoA-2R for AOB amoA.23 Reactions were conducted on a Bori 9600plus fluorescence quantitative PCR instrument using previously reported thermal profiles (Supplementary Table S2). Triplicate PCR assays were performed the appropriately diluted samples (10–30 ng μL−1) and 10-fold serially diluted plasmid standards as described by Guo et al.24. Plasmid standards containing the different amoA variants were obtained by TA-cloning with subsequent plasmid DNA extraction using the Easy Pure Plasmid MiniPrep Kit (TransGen Biotech, China). Standard curves covered three to eight orders of magnitude with R2 greater than 0.999. The efficiency of qPCR was about 95%.Library construction and metagenomic sequencingThe extracted DNA was fragmented to an average size of about 400 bp using Covaris M220 (Gene Company Limited, China) for paired-end library construction. A paired-end library was constructed using NEXTFLEX Rapid DNA-Seq (Bioo Scientific, Austin, TX, USA). Adapters containing the full complement of sequencing primer hybridization sites were ligated to the blunt-end of fragments. Paired-end sequencing was performed on Illumina NovaSeq PE150 (Illumina Inc., San Diego, CA, USA) at Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) using NovaSeq Reagent Kits according to the manufacturer’s instructions (www.illumina.com).Metagenomic assembly and genome binningRaw metagenomic sequencing reads (in PE150 mode) were trimmed and quality filtered with in-house Perl scripts as described previously.25 Briefly, duplicated reads caused by the PCR bias during the amplification step were dereplicated. Reads were eliminated if both paired-end reads contained >10% ambiguous bases (that is, “N”). Low-quality bases with phred values 2.5 kbp were retained for later analysis. Genome binning was conducted for each sample using sequencing depth and tetranucleotide frequency. To calculate coverage, high-quality reads from all samples were mapped to the contigs using BBMap v38.85 (http://sourceforge.net/projects/bbmap/) with minimal identity set to 90%. The generated bam files were sorted using samtools v1.3.1.27 Then, sequencing depth was calculated using the script “jgi_summarize_bam_contig_depths” in MetaBAT.28 Metagenome-assembled genomes (MAGs) were obtained in MetaBAT. MAG quality, including completeness, contamination, and heterogeneity, was estimated using CheckM v1.0.12.29 To optimize the MAGs, emergent self-organizing maps30 were used to visualize the bins, and contigs with abnormal coverage or discordant tetranucleotide frequencies were removed manually. Finally, all MAGs were reassembled using SPAdes with the following parameters: –careful –k 21,33,55,77,99,127. The reads used for reassembly were recruited by mapping all high-quality reads to each MAG using BBMap with the same parameter settings as described above.Functional annotation of metagenomic assemblies and metagenome-assembled genomesGene calling was conducted for the complete metagenomic assemblies and all retrieved MAGs using Prodigal v2.6.3.31 For the MAGs, predicted protein-coding sequences (CDSs) were subsequently aligned to a manually curated database containing amoCAB, hao, and nxrAB genes collected from public database using DIAMOND v0.7.9 (E-values < 1e−5 32) MAGs found to contain all these genes were labeled as comammox Nitrospira MAGs and kept for later analysis. Functional annotations were obtained by searching all CDSs in the complete metagenomic assemblies and the retrieved MAGs against the NCBI-nr, eggNOG, and KEGG databases using DIAMOND (E-values < 1e−5).Phylogenetic analysisPhylogenomic treeThe taxonomic assignment of the three identified comammox Nitrospira MAGs was determined using GTDB-tk v0.2.2.33 To reveal the phylogenetic placement of these MAGs within the Nitrospirae, 296 genomes from this phylum were downloaded from the NCBI-RefSeq database. The download genomes were dereplicated using dRep v2.3.234 (-con 10 -comp 80) to reduce the complexity and redundancy of the phylogenetic tree, which resulted in the removal of 166 genomes. In the remaining genomes, the three comammox Nitrospira MAGs and 25 genomes from phylum Thermotogae which were treated as outgroups, a set of 16 ribosomal proteins were identified using AMPHORA2.35 Each gene set was aligned separately using MUSCLE v3.8.31 with default parameters,36 and poorly aligned regions were filtered by TrimAl v1.4.rev22 (-gt 0.95 –cons 5037) The individual alignments of the 16 marker genes were concatenated, resulting in an alignment containing 118 species and 2665 amino acid positions. Subsequently, the best phylogenetic model LG + F + R8 was determined using ModelFinder38 integrated into IQ-tree v1.6.10.39 Finally, a phylogenetic tree was reconstructed using IQ-tree with the following options: -bb 1000 –alrt 1000. The generated tree in newick format was visualized by iTOL v3.40 amoA treeReference amoA sequences of AOB, AOA, and comammox Nitrospira were obtained from NCBI. Together with the amoA genes from the present study, all sequences were aligned and trimmed as described above. IQ-tree was used to generate the phylogenetic tree, with “LG + G4” determined as the best model.ureABC gene treeureABC gene sequences detected in this study were extracted and used to build a database using “hmmbuild” command in HMMER.41 ureABC gene sequences from genomes in NCBI-RefSeq database (downloaded on July 1st, 2019) were identified by searching against the built database using AMPHORA2. The same procedures as above were conducted to construct the phylogenetic tree of concatenated ureABC genes, except for the sequence collection step. To reduce the complexity of the phylogenetic tree, the alignment of concatenated ureABC genes was clustered using CD-HIT42 with the following parameters: -aS 1 -c 0.8 -g 1. Only representative sequences were kept for phylogeny reconstruction, which resulted in an alignment containing 858 sequences and 1263 amino acids positions. “LG + R10” was determined as the best model and used to build the phylogenetic tree. Regarding the Nitrospirae-specific ureABC gene tree, ureABC gene sequences were recruited from the genomes as described above, but without the sequence clustering step. The final Nitrospirae-specific phylogeny of ureABC genes was built on an alignment containing 62 sequences and 1015 amino acid positions with “LG + F + I + G4” as the best model. More

1.Bennett, J. R. et al. Polar lessons learned: long-term management based on shared threats in Arctic and Antarctic environments. Front. Ecol. Environ. 13, 316–324 (2015).Article 

Google Scholar 
2.Pyšek, P. et al. Scientists’ warning on invasive alien species. Biol. Rev. 95, 1511–1534 (2020).PubMed 
Article 

Google Scholar 
3.Convey, P. et al. The spatial structure of Antarctic biodiversity. Ecol. Monogr. 84, 203–244 (2014).Article 

Google Scholar 
4.Turner, J. et al. Antarctic climate change and the environment: an update. Polar Rec. 50, 237–259 (2014).Article 

Google Scholar 
5.Siegert, M., et al. The Antarctic Peninsula under a 1.5 °C global warming scenario. Front. Environ. Sci. 7 (2019).6.Huiskes, A. H. L. et al. Aliens in Antarctica: assessing transfer of plant propagules by human visitors to reduce invasion risk. Biol. Conserv. 171, 278–284 (2014).Article 

Google Scholar 
7.Hughes, K. A., Pertierra, L. R., Molina-Montenegro, M. A. & Convey, P. Biological invasions in terrestrial Antarctica: what is the current status and can we respond? Biodivers. Conserv. 24, 1031–1055 (2015).Article 

Google Scholar 
8.Molina-Montenegro, M. A., et al. Assessing the importance of human activities for the establishment of the invasive Poa annua in Antarctica. Polar Res. 33, https://doi.org/10.3402/polar.v33.21425 (2014).9.Whinam, J., Chilcott, N. & Bergstrom, D. M. Subantarctic hitchhikers: expeditioners as vectors for the introduction of alien organisms. Biol. Conserv. 121, 207–219 (2005).Article 

Google Scholar 
10.Hughes, K. A. et al. Invasive non-native species likely to threaten biodiversity and ecosystems in the Antarctic Peninsula region. Glob. Change Biol. 26, 2702–2716 (2020).Article 

Google Scholar 
11.Osyczka, P. Alien lichens unintentionally transported to the “Arctowski” station (South Shetlands, Antarctica). Polar Biol. 33, 1067–1073 (2010).Article 

Google Scholar 
12.Chown, S. L. et al. Continent-wide risk assessment for the establishment of nonindigenous species in Antarctica. Proc. Natl Acad. Sci. USA 109, 4938–4943 (2012).CAS 
PubMed 
Article 

Google Scholar 
13.Hughes, K. A., Greenslade, P. & Convey, P. The fate of the non-native Collembolon, Hypogastrura viatica, at the southern extent of its introduced range in Antarctica. Polar Biol. 40, 2127–2131 (2017).Article 

Google Scholar 
14.Lee, J. E. & Chown, S. L. Breaching the dispersal barrier to invasion: quantification and management. Ecol. Appl. 19, 1944–1959 (2009).PubMed 
Article 

Google Scholar 
15.Tsujimoto, M. & Imura, S. Does a new transportation system increase the risk of importing non-native species to Antarctica? Antarct. Sci. 24, 441–449 (2012).Article 

Google Scholar 
16.Duffy, G. A. et al. Barriers to globally invasive species are weakening across the Antarctic. Divers. Distrib. 23, 982–996 (2017).Article 

Google Scholar 
17.Convey, P., Hopkins, D. W., Roberts, S. J. & Tyler, A. N. Global southern limit of flowering plants and moss peat accumulation. Polar Res. 30, 8929 (2011).Article 

Google Scholar 
18.Bergstrom, D. M. & Chown, S. L. Life at the front: history, ecology and change on southern ocean islands. Trends Ecol. Evolut. 14, 472–477 (1999).CAS 
Article 

Google Scholar 
19.Gremmen, N. J. M., Chown, S. L. & Marshall, D. J. Impact of the introduced grass Agrostis stolonifera on vegetation and soil fauna communities at Marion Island, sub-Antarctic. Biol. Conserv. 85, 223–231 (1998).Article 

Google Scholar 
20.Cavieres, L. A., Sanhueza, A. K., Torres-Mellado, G. & Casanova-Katny, A. Competition between native Antarctic vascular plants and invasive Poa annua changes with temperature and soil nitrogen availability. Biol. Invasions 20, 1597–1610 (2018).Article 

Google Scholar 
21.Molina-Montenegro, M. A., et al. Increasing impacts by Antarctica’s most widespread invasive plant species as result of direct competition with native vascular plants. Neobiota 51, 19–40 (2019).22.Molina-Montenegro, M. A. et al. Occurrence of the non-native annual bluegrass on the Antarctic mainland and its negative effects on native plants. Conserv. Biol. 26, 717–723 (2012).PubMed 
Article 

Google Scholar 
23.Frenot, Y. et al. Biological invasions in the Antarctic: extent, impacts and implications. Biol. Rev. 80, 45–72 (2005).PubMed 
Article 

Google Scholar 
24.Leihy, R. I., Duffy, G. A. & Chown, S. L. Species richness and turnover among indigenous and introduced plants and insects of the Southern Ocean Islands. Ecosphere 9, 15 (2018).Article 

Google Scholar 
25.Graae, B. J. et al. On the use of weather data in ecological studies along altitudinal and latitudinal gradients. Oikos 121, 3–19 (2012).Article 

Google Scholar 
26.Convey, P., Coulson, S. J., Worland, M. R. & Sjöblom, A. The importance of understanding annual and shorter-term temperature patterns and variation in the surface levels of polar soils for terrestrial biota. Polar Biol. 41, 1587–1605 (2018).Article 

Google Scholar 
27.Edwards, J. A. An experimental introduction of vascular plants from South Georgia to the Maritime Antarctic. Br. Antarct. Surv. Bull. 49, 73–80 (1979).
Google Scholar 
28.Corte, A. La primera fanerogama adventicia hallada en el continente Antartico. Inst. Antártico Argent. 62, 1–14 (1961).
Google Scholar 
29.Pertierra, L. R. et al. Global thermal niche models of two European grasses show high invasion risks in Antarctica. Glob. Change Biol. 23, 2863–2873 (2017).Article 

Google Scholar 
30.Macloskie, G. The Patagonian flora. Plant World 10, 97–103 (1907).
Google Scholar 
31.Pertierra, L. R. et al. Assessing the invasive risk of two non-native Agrostis species on sub-Antarctic Macquarie Island. Polar Biol. 39, 2361–2371 (2016).Article 

Google Scholar 
32.Bokhorst, S. et al. Climate change effects on soil arthropod communities from the Falkland Islands and the Maritime Antarctic. Soil Biol. Biochem. 40, 1547–1556 (2008).CAS 
Article 

Google Scholar 
33.Chwedorzewska, K. J. et al. Poa annua L. in the maritime Antarctic: an overview. Polar Rec. 51, 637–643 (2015).Article 

Google Scholar 
34.Zhang, H. et al. Is the proportion of clonal species higher at higher latitudes in Australia? Austral. Ecol. 43, 69–75 (2018).Article 

Google Scholar 
35.Holtom, A. & Greene, S. W. The growth and reproduction of Antarctic flowering plants. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 252, 323–337 (1967).
Google Scholar 
36.Vera, M. L. Colonization and demographic structure of Deschampsia antarctica and Colobanthus quitensis along an altitudinal gradient on Livingston Island, South Shetland Islands, Antarctica. Polar Res. 30, 7146 (2011).Article 

Google Scholar 
37.Convey, P. The influence of environmental characteristics on life history attributes of Antarctic terrestrial biota. Biol. Rev. Camb. Philos. Soc. 71, 191–225 (1996).Article 

Google Scholar 
38.Pertierra, L. R., Lara, F., Benayas, J. & Hughes, K. A. Poa pratensis L., current status of the longest-established non-native vascular plant in the Antarctic. Polar Biol. 36, 1473–1481 (2013).Article 

Google Scholar 
39.Williams, L. K. et al. Longevity, growth and community ecology of invasive Poa annua across environmental gradients in the subantarctic. Basic Appl. Ecol. 29, 20–31 (2018).Article 

Google Scholar 
40.Pertierra, L. et al. Eradication of the non-native Poa pratensis colony at Cierva Point, Antarctica: a case study of international cooperation and practical management in an area under multi-party governance. Environ. Sci. Policy 69, 50–56 (2016).Article 

Google Scholar 
41.Hughes, K. A. & Convey, P. The protection of Antarctic terrestrial ecosystems from inter- and intra-continental transfer of non-indigenous species by human activities: a review of current systems and practices. Glob. Environ. Change 20, 96–112 (2010).Article 

Google Scholar 
42.Smith, R. I. L. Introduced plants in Antarctica: potential impacts and conservation issues. Biol. Conserv. 76, 135–146 (1996).Article 

Google Scholar 
43.Thompson, K., Grime, J. P. & Mason, G. Seed germination in response to diurnal fluctuations of temperature. Nature 267, 147–149 (1977).CAS 
PubMed 
Article 

Google Scholar 
44.McGeoch, M. A. et al. Monitoring biological invasion across the broader Antarctic: a baseline and indicator framework. Glob. Environ. Change 32, 108–125 (2015).Article 

Google Scholar 
45.Kellmann-Sopyła, W. & Giełwanowska, I. Germination capacity of five polar Caryophyllaceae and Poaceae species under different temperature conditions. Polar Biol. 38, 1753–1765 (2015).Article 

Google Scholar 
46.Körner, C. Alpine Treelines: Functional Ecology of the Global High Elevation Tree Limits. 1–220 (2012).47.Elmendorf, S. C. et al. Plot-scale evidence of tundra vegetation change and links to recent summer warming. Nat. Clim. Change 2, 453–457 (2012).Article 

Google Scholar 
48.Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).CAS 
Article 
PubMed 

Google Scholar 
49.Billings, W. D. Constraints to plant growth, reproduction, and establishment in Arctic environments. Arct. Alp. Res. 19, 357–365 (1987).Article 

Google Scholar 
50.Block, W., Smith, R. I. L. & Kennedy, A. D. Strategies of survival and resource exploitation in the Antarctic fellfield ecosystem. Biol. Rev. 84, 449–484 (2009).CAS 
PubMed 
Article 

Google Scholar 
51.Aerts, R. & Chapin, F. S. The mineral nutrition of wild plants revisited: a re-evaluation of processes and patterns. Adv. Ecol. Res. 30, 1–67 (2000).CAS 

Google Scholar 
52.Barrand, N. E. et al. Trends in Antarctic Peninsula surface melting conditions from observations and regional climate modeling. J. Geophys. Res. 118, 315–330 (2013).Article 

Google Scholar 
53.Walton, D. W. H. The Signy Island terrestrial reference sites: XV. Micro-climate monitoring, 1972-1974. Br. Antarct. Surv. Bull. 55, 111–126 (1982).
Google Scholar 
54.Smith, R. I. L. Bryophyte oases in Ablation Valleys on Alexander Island, Antarctica. Bryologist 91, 45–50 (1988).55.Hunt, H. W., Fountain, A. G., Doran, P. T. & Basagic, H. A dynamic physical model for soil temperature and water in Taylor Valley, Antarctica. Antarct. Sci. 22, 419–434 (2010).Article 

Google Scholar 
56.Bracegirdle, T. J., Barrand, N. E., Kusahara, K. & Wainer, I. Predicting Antarctic climate using climate models. Antarctic Environ. Portal https://doi.org/10.18124/5wq2-0154 (2016).57.De Boeck, H. J., De Groote, T. & Nijs, I. Leaf temperatures in glasshouses and open-top chambers. N. Phytol. 194, 1155–1164 (2012).Article 

Google Scholar 
58.Greenspan, S. E. et al. Low-cost fluctuating-temperature chamber for experimental ecology. Methods Ecol. Evolut. 7, 1567–1574 (2016).Article 

Google Scholar 
59.Bokhorst, S. et al. Contrasting survival and physiological responses of sub-Arctic plant types to extreme winter warming and nitrogen. Planta 247, 635–648 (2018).CAS 
PubMed 
Article 

Google Scholar 
60.Litaor, M. I., Williams, M. & Seastedt, T. R. Topographic controls on snow distribution, soil moisture, and species diversity of herbaceous alpine vegetation, Niwot Ridge, Colorado. J. Geophys. Res. 113 (2008).61.Taulavuori, K., Sarala, M. & Taulavuori, E. Growth responses of trees to Arctic light environment. (eds U. Lüttge, W. Beyschlag, B. Büdel, and D. Francis). 157–168. (Springer, Berlin).62.Goncharova, O. et al. Influence of snow cover on soil temperatures: meso- and micro-scale topographic effects (a case study from the northern West Siberia discontinuous permafrost zone). Catena 183, 104224 (2019).Article 

Google Scholar 
63.Upson, R., et al. Field Guide to the Introduced Flora of South Georgia. (Royal Botanical Gardens, Kew, 2017).64.Allen, S. E. & Heal, O. W. Soils of the Maritime Antarctic zone. (eds M. W. Holdgate). 693–696, (Academic Press, London, 1970).65.Bölter, M. Soil development and soil biology on King George Island, Maritime Antarctic. Pol. Polar Res. 32, 105–116 (2011).Article 

Google Scholar 
66.Duffy, G. A. & Lee, J. R. Ice-free area expansion compounds the non-native species threat to Antarctic terrestrial biodiversity. Biol. Conserv. 232, 253–257 (2019).Article 

Google Scholar 
67.Lee, J. R. et al. Climate change drives expansion of Antarctic ice-free habitat. Nature 547, 49–54 (2017).CAS 
PubMed 
Article 

Google Scholar 
68.Bokhorst, S., Huiskes, A., Convey, P. & Aerts, R. The effect of environmental change on vascular plant and cryptogam communities from the Falkland Islands and the Maritime Antarctic. BMC Ecol. 7, 15 (2007).PubMed 
PubMed Central 
Article 

Google Scholar 
69.IPCC, Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. (ed T. F. Stocker, et al.) 1535 (Cambridge, 2013).70.Bracegirdle, T. J. et al. Back to the future: using long-term observational and paleo-proxy reconstructions to improve model projections of antarctic climate. Geosciences 9, 255 (2019).Article 

Google Scholar 
71.Royles, J. et al. Carbon isotope evidence for recent climate-related enhancement of CO2 assimilation and peat accumulation rates in Antarctica. Glob. Change Biol. 18, 3112–3124 (2012).Article 

Google Scholar 
72.Tang, M. S. Y. et al. Precipitation instruments at Rothera Station, Antarctic Peninsula: a comparative study. Polar Res. 37, 1503906 (2018).Article 

Google Scholar 
73.Bokhorst, S. et al. Microclimate impacts of passive warming methods in Antarctica: implications for climate change studies. Polar Biol. 34, 1421–1435 (2011).Article 

Google Scholar 
74.Hiltbrunner, E. et al. Ecological consequences of the expansion of N2-fixing plants in cold biomes. Oecologia 176, 11–24 (2014).PubMed 
Article 

Google Scholar 
75.Convey, P., et al. Microclimate data from Anchorage Island, 2001–2009. (2020).76.Convey, P., et al. Microclimate data from Coal Nunatak, 2006–2019. (2020).77.Convey, P., et al. Microclimate data from Mars Oasis, 2000–2019. (2020).78.Moore, D. M. The vascular Flora of the Falkland Islands. Br. Antarct. Surv. Sci. Rep. 60, 4–202 (1968).
Google Scholar 
79.Oliva, M. et al. Recent regional climate cooling on the Antarctic Peninsula and associated impacts on the cryosphere. Sci. Total Environ. 580, 210–223 (2017).CAS 
PubMed 
Article 

Google Scholar 
80.Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob. Ecol. Biogeogr. 12, 361–371 (2003).Article 

Google Scholar 
81.RCoreTeam, R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, 2015).82.Bokhorst, S., Convey, P., Casanova, A. & Aerts, R. Warming impacts on potential germination of non-native plants on the Antarctic Peninsula. https://npdc.nl/dataset/d350edc1-e31e-51b9-aa37-d11365e6bc2b (2020). More

1.Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) Summary for Policymakers of the Global Assessment Report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (eds Díaz, S. et al.) (IPBES secretariat, 2019).2.Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).Article 
CAS 

Google Scholar 
3.Adams, W. M. Against Extinction: The Story of Conservation (Earthscan, 2004).4.Escobar, A. Whose knowledge, whose nature? Biodiversity, conservation, and the political ecology of social movements. J. Polit. Ecol. 5, 53–82 (1998).
Google Scholar 
5.Meine, C., Soulé, M. & Noss, R. F. A mission-driven discipline: the growth of conservation biology. Conserv. Biol. 20, 631–651 (2006).Article 

Google Scholar 
6.Sandbrook, C., Fisher, J. A., Holmes, G., Luque-Lora, R. & Keane, A. The global conservation movement is diverse but not divided. Nat. Sustain. 2, 316–323 (2019).Article 

Google Scholar 
7.Takacs, D. The Idea of Biodiversity: Philosophies of Paradise (Johns Hopkins Univ. Press, 1996).8.Garland, E. The elephant in the room: confronting the colonial character of wildlife conservation in Africa. Afr. Stud. Rev 51, 51–74 (2008).Article 

Google Scholar 
9.Thekaekara, T. Botswana elephants episode: there’s a colonial underpinning to conservation. DownToEarth (22 July 2020); https://www.downtoearth.org.in/blog/wildlife-and-biodiversity/botswana-elephants-episode-there-s-a-colonial-underpinning-to-conservation-7242910.Cronon, W. et al. Uncommon Ground: Toward Reinventing Nature (WW Norton & Company, 1995).
Google Scholar 
11.Stephens, L. et al. Archaeological assessment reveals Earth’s early transformation through land use. Science 365, 897–902 (2019).CAS 
Article 

Google Scholar 
12.Brockington, D., Duffy, R. & Igoe, J. Nature Unbound: Conservation, Capitalism and the Future of Protected Areas (Earthscan, 2008).13.Mace, G. M. Whose conservation? Science 345, 1558–1560 (2014).CAS 
Article 

Google Scholar 
14.Mace, G. M., Norris, K. & Fitter, A. H. Biodiversity and ecosystem services: a multilayered relationship. Trends Ecol. Evol. 27, 19–26 (2012).Article 

Google Scholar 
15.Lele, S., Springate-Baginski, O., Lakerveld, R., Deb, D. & Dash, P. Ecosystem services: origins, contributions, pitfalls, and alternatives. Conserv. Soc. 11, 343–358 (2013).Article 

Google Scholar 
16.Martin, J.-L., Maris, V. & Simberloff, D. S. The need to respect nature and its limits challenges society and conservation science. Proc. Natl Acad. Sci. USA 113, 6105–6112 (2016).CAS 
Article 

Google Scholar 
17.Díaz, S. et al. The IPBES Conceptual Framework: connecting nature and people. Curr. Opin. Environ. Sustain 14, 1–16 (2015).Article 

Google Scholar 
18.Turnhout, E., Waterton, C., Neves, K. & Buizer, M. Rethinking biodiversity: from goods and services to ‘living with’. Conserv. Lett. 6, 154–161 (2013).Article 

Google Scholar 
19.Kenter, J. O. et al. Loving the mess: navigating diversity and conflict in social values for sustainability. Sustain. Sci. 14, 1439–1461 (2019).Article 

Google Scholar 
20.Lele, S. From wildlife-ism to ecosystem-service-ism to a broader environmentalism. Environ. Conserv. https://doi.org/10.1017/S0376892920000466 (2020).21.Muradian, R. & Pascual, U. A typology of elementary forms of human-nature relations: a contribution to the valuation debate. Curr. Opin. Environ. Sustain 35, 8–14 (2018).Article 

Google Scholar 
22.Robertson, D. P. & Hull, R. B. Beyond biology: toward a more public ecology for conservation. Conserv. Biol. 15, 970–979 (2001).Article 

Google Scholar 
23.Tallis, H. & Lubchenco, J. Working together: a call for inclusive conservation. Nature 515, 27 (2014).CAS 
Article 

Google Scholar 
24.Kareiva, P. M., Marvier, M. & Silliman, B. Effective Conservation Science: Data Not Dogma (Oxford Univ. Press, 2018).25.Wilshusen, P. R., Brechin, S. R., Fortwangler, C. L. & West, P. C. Reinventing a square wheel: critique of a resurgent “protection paradigm” in international biodiversity conservation. Soc. Nat. Resour. 15, 17–40 (2002).Article 

Google Scholar 
26.Turnhout, E. The politics of environmental knowledge. Conserv. Soc. 16, 363–371 (2018).Article 

Google Scholar 
27.Louder, E. & Wyborn, C. Biodiversity narratives: stories of the evolving conservation landscape. Environ. Conserv. 47, 251–259 (2020).Article 

Google Scholar 
28.Gadgil, M., Seshagiri Rao, P., Utkarsh, G., Pramod, P. & Chhatre, A. New meanings for old knowledge: the people’s biodiversity registers program. Ecol. Appl. 10, 1307–1317 (2000).Article 

Google Scholar 
29.Buijs, A. E., Fischer, A., Rink, D. & Young, J. C. Looking beyond superficial knowledge gaps: understanding public representations of biodiversity. Int. J. Biodivers. Sci. Manag. 4, 65–80 (2008).Article 

Google Scholar 
30.Wyborn, C. et al. An agenda for research and action towards diverse and just futures for life on Earth. Conserv. Biol. https://doi.org/10.1111/cobi.13671 (2020).31.Wyborn, C. et al. Imagining transformative biodiversity futures. Nat. Sustain. 3, 670–672 (2020).Article 

Google Scholar 
32.Samper, C. Planetary boundaries: rethinking biodiversity. Nat. Clim. Change 1, 118–119 (2009).Article 

Google Scholar 
33.Mayer, P. Biodiversity: the appreciation of different thought styles and values helps to clarify the term. Restor. Ecol. 14, 105–111 (2006).Article 

Google Scholar 
34.Morar, N., Toadvine, T. & Bohannan, B. J. Biodiversity at twenty-five years: revolution or red herring? Ethics Policy Environ. 18, 16–29 (2015).Article 

Google Scholar 
35.Purvis, A. et al. in Global Assessment Report on Biodiversity and Ecosystem Services (eds Brondízio, E. S. et al.) Ch. 2.2 (Secretariat of the Intergovernmental Science-Policy Platform for Biodiversity and Ecosystem Services, 2019).36.Dasgupta, P. The Economics of Biodiversity: The Dasgupta Review (HM Treasury, 2021).37.Perrings, C. Our Uncommon Heritage: Biodiversity Change, Ecosystem Services, and Human Well-Being (Cambridge Univ. Press, 2014).38.Gowdy, J. M. The value of biodiversity: markets, society, and ecosystems. Land Econ. 73, 25–41 (1997).Article 

Google Scholar 
39.Keulartz, J. Boundary work in ecological restoration. Environ. Phil. 6, 35–55 (2009).Article 

Google Scholar 
40.Chan, K. M. et al. Why protect nature? Rethinking values and the environment. Proc. Natl Acad. Sci. USA 113, 1462–1465 (2016).CAS 
Article 

Google Scholar 
41.Descola, P. The Ecology of Others (Prickly Paradigm, 2013).42.Raffles, R. Intimate knowledge. Int. Soc. Sci. J. 54, 325–335 (2002).Article 

Google Scholar 
43.Tengö, M., Brondizio, E. S., Elmqvist, T., Malmer, P. & Spierenburg, M. Connecting diverse knowledge systems for enhanced ecosystem governance: the multiple evidence base approach. AMBIO 43, 579–591 (2014).Article 

Google Scholar 
44.Zafra-Calvo, N. et al. Plural valuation of nature for equity and sustainability: insights from the Global South. Glob. Environ. Change 63, 102115 (2020).Article 

Google Scholar 
45.Lele, S., Wilshusen, P., Brockington, D., Seidler, R. & Bawa, K. Beyond exclusion: alternative approaches to biodiversity conservation in the developing tropics. Curr. Opin. Environ. Sustain. 2, 94–100 (2010).Article 

Google Scholar 
46.Pascual, U. et al. Social equity matters in payments for ecosystem services. BioScience 64, 1027–1036 (2014).Article 

Google Scholar 
47.Wunder, S. et al. From principles to practice in paying for nature’s services. Nat. Sustain. 1, 145–150 (2018).Article 

Google Scholar 
48.Büscher, B. et al. Half-Earth or whole Earth? Radical ideas for conservation, and their implications. Oryx 51, 407–410 (2017).Article 

Google Scholar 
49.Adams, W. M. in The Anthropology of Sustainability, Palgrave Studies in Anthropology of Sustainability (eds Brightman, M. & Lewis, J.) 111–126 (Palgrave Macmillan, 2017).50.Vatn, A. An institutional analysis of methods for environmental appraisal. Ecol. Econ. 68, 2207–2215 (2009).Article 

Google Scholar 
51.Büscher, B., Sullivan, S., Neves, K., Igoe, J. & Brockington, D. Towards a synthesized critique of neoliberal biodiversity conservation. Capital. Nat. Social. 23, 4–30 (2012).Article 

Google Scholar 
52.Lliso, B., Mariel, P., Pascual, U. & Engel, S. Increasing the credibility and salience of valuation through deliberation: lessons from the Global South. Glob. Environ. Change 62, 102065 (2020).Article 

Google Scholar 
53.Rudel, T. K., Defries, R., Asner, G. P. & Laurance, W. F. Changing drivers of deforestation and new opportunities for conservation. Conserv. Biol. 23, 1396–1405 (2009).Article 

Google Scholar 
54.Mazor, T. et al. Global mismatch of policy and research on drivers of biodiversity loss. Nat. Ecol. Evol. 2, 1071–1074 (2018).Article 

Google Scholar 
55.Maxwell, S. L., Fuller, R. A., Brooks, T. M. & Watson, J. E. Biodiversity: the ravages of guns, nets and bulldozers. Nature 536, 143–145 (2016).CAS 
Article 

Google Scholar 
56.Folke, C. et al. Transnational corporations and the challenge of biosphere stewardship. Nat. Ecol. Evol. 3, 1396–1403 (2019).Article 

Google Scholar 
57.Ceddia, M. G. Investments’ role in ecosystem degradation. Science 368, 377–377 (2020).
Google Scholar 
58.Neumann, R. P. Moral and discursive geographies in the war for biodiversity in Africa. Polit. Geogr. 23, 813–837 (2004).Article 

Google Scholar 
59.Wiedmann, T., Lenzen, M., Keyßer, L. T. & Steinberger, J. K. Scientists’ warning on affluence. Nat. Commun. 11, 3107 (2020).CAS 
Article 

Google Scholar 
60.Svarstad, H., Petersen, L. K., Rothman, D., Siepel, H. & Wätzold, F. Discursive biases of the environmental research framework DPSIR. Land Use Policy 25, 116–125 (2008).Article 

Google Scholar 
61.Gari, S. R., Newton, A. & Icely, J. D. A review of the application and evolution of the DPSIR framework with an emphasis on coastal social-ecological systems. Ocean Coast. Manage. 103, 63–77 (2015).Article 

Google Scholar 
62.Muradian, R. et al. Payments for ecosystem services and the fatal attraction of win-win solutions. Conserv. Lett. 6, 274–279 (2013).Article 

Google Scholar 
63.Otero, I. et al. Biodiversity policy beyond economic growth. Conserv. Lett. 13, e12713 (2020).Article 

Google Scholar 
64.Nielsen, J. Ø. et al. Toward a normative land systems science. Curr. Opin. Environ. Sustain. 38, 1–6 (2019).Article 

Google Scholar 
65.Lele, S. & Kurien, A. Interdisciplinary analysis of the environment: insights from tropical forest research. Environ. Conserv. 38, 211–233 (2011).Article 

Google Scholar 
66.West, S., Haider, L. J., Stålhammar, S. & Woroniecki, S. A relational turn for sustainability science? Relational thinking, leverage points and transformations. Ecosyst. People 16, 304–325 (2020).Article 

Google Scholar 
67.Boivin, N. L. et al. Ecological consequences of human niche construction: examining long-term anthropogenic shaping of global species distributions. Proc. Natl Acad. Sci. USA 113, 6388–6396 (2016).CAS 
Article 

Google Scholar 
68.Jacobs, S. et al. Use your power for good: plural valuation of nature – the Oaxaca statement. Glob. Sustain. 3, e8 (2020).Article 

Google Scholar 
69.Turnhout, E., Tuinstra, W. & Halffman, W. Environmental Expertise: Connecting Science, Policy and Society (Cambridge Univ. Press, 2019).70.Saberwal, V. & Chhatre, A. Democratizing Nature: Politics, Conservation, and Development in India (Oxford Univ. Press, 2006). More

1.Thresher, R. E., Colin, P. L. & Bell, L. J. Planktonic duration, distribution and population structure of western and Central Pacific Damselfishes (Pomacentridae). Copeia 420–434, 1989. https://doi.org/10.2307/1445439 (1989).Article 

Google Scholar 
2.Leis, J. M. Behaviour as input for modelling dispersal of fish larvae: Behaviour, biogeography, hydrodynamics, ontogeny, physiology and phylogeny meet hydrography. Mar. Ecol. Prog. Ser. 347, 185–194. https://doi.org/10.3354/meps06977 (2007).Article 
ADS 

Google Scholar 
3.Fisher, R., Leis, J. M., Clark, D. L. & Wilson, S. K. Critical swimming speeds of late-stage coral reef fish larvae: Variation within species, among species and between locations. Mar. Biol. 147, 1201–1212. https://doi.org/10.1007/s00227-005-0001-x (2005).Article 

Google Scholar 
4.Stobutzki, I. & Bellwood, D. Sustained swimming abilities of the late pelagic stages of coral reef fishes. Mar. Ecol. Prog. Ser. 149, 35–41. https://doi.org/10.3354/meps149035 (1997).Article 
ADS 

Google Scholar 
5.Gerlach, G., Atema, J., Kingsford, M. J., Black, K. P. & Miller-Sims, V. Smelling home can prevent dispersal of reef fish larvae. Proc. Natl. Acad. Sci. 104, 858–863. https://doi.org/10.1073/pnas.0606777104 (2007).CAS 
Article 
PubMed 
ADS 

Google Scholar 
6.Almany, G. R., Berumen, M. L., Thorrold, S. R., Planes, S. & Jones, G. P. Local Replenishment of Coral Reef fish populations in a Marine Reserve. Science 316, 742–744. https://doi.org/10.1126/science.1140597 (2007).CAS 
Article 
PubMed 
ADS 

Google Scholar 
7.Jones, G. P., Planes, S. & Thorrold, S. R. Coral Reef Fish Larvae Settle Close to Home. Curr. Biol. 15, 1314–1318. https://doi.org/10.1016/j.cub.2005.06.061 (2005).CAS 
Article 
PubMed 

Google Scholar 
8.Kingsford, M. J. et al. Sensory environments, larval abilities and local self-recruitment. Bull. Mar. Sci. 70, 309–340 (2002).
Google Scholar 
9.Mouritsen, H., Atema, J., Kingsford, M. J. & Gerlach, G. Sun compass orientation helps coral reef fish larvae return to their natal reef. PLoS ONE. https://doi.org/10.1371/journal.pone.0066039 (2013).10.Dufour, V. & Galzin, R. Colonization patterns of reef fish larvae to the lagoon at Moorea Island, French Polynesia. Mar. Ecol. Prog. Ser. 102, 143–152. https://doi.org/10.3354/meps102143 (1993).Article 
ADS 

Google Scholar 
11.Holbrook, S. & Schmitt, R. Settlement patterns and process in a coral reef damselfish: In situ nocturnal observations using infrared video. In Proceedings of the 8th International Coral Reef Symposium, Vol. 2, 1143–1148 (1997).12.Leis, J. M. & Carson-Ewart, B. M. Complex behaviour by coral-reef fish larvae in open-water and near-reef pelagic environments. Environ. Biol. Fishes 53, 259–266. https://doi.org/10.1023/A:1007424719764 (1998).Article 

Google Scholar 
13.Litsios, G. et al. Mutualism with sea anemones triggered the adaptive radiation of clownfishes. BMC Evol. Biol. 12, 212. https://doi.org/10.1186/1471-2148-12-212 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
14.Bridge, T., Scott, A. & Steinberg, D. Abundance and diversity of anemonefishes and their host sea anemones at two mesophotic sites on the Great Barrier Reef, Australia. Coral Reefs 31, 1057–1062. https://doi.org/10.1007/s00338-012-0916-x (2012).Article 
ADS 

Google Scholar 
15.Mariscal, R. N. Behavior of symbiotic fishes and sea anemones. In Winn, H. E. & Olla, B. L. (eds.) Behavior of Marine Animals, 327–360 (Springer US, 1972). https://doi.org/10.1007/978-1-4684-0910-9_4.16.Tauber, E., Last, K. S., Olive, P. J. & Kyriacou, C. P. Clock gene evolution and functional divergence. J. Biol. Rhythm. 19, 445–458. https://doi.org/10.1177/0748730404268775 (2004).CAS 
Article 

Google Scholar 
17.Emran, F., Rihel, J., Adolph, A. R. & Dowling, J. E. Zebrafish larvae lose vision at night. Proc. Natl. Acad. Sci. 107, 6034–6039. https://doi.org/10.1073/pnas.0914718107 (2010).Article 
PubMed 
ADS 

Google Scholar 
18.Cahill, G. M., Hurd, M. W. & Batchelor, M. M. Circadian rhythmicity in the locomotor activity of larval zebrafish. NeuroReport 9, 3445–3449. https://doi.org/10.1097/00001756-199810260-00020 (1998).CAS 
Article 
PubMed 

Google Scholar 
19.Ceinos, R. M. et al. Mutations in blind cavefish target the light-regulated circadian clock gene, period 2. Sci. Rep. 8, 8754. https://doi.org/10.1038/s41598-018-27080-2 (2018).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
20.Frøland Steindal, I. & Whitmore, D. Circadian clocks in fish—What have we learned so far?. Biology 8, 17. https://doi.org/10.3390/biology8010017 (2019).CAS 
Article 
PubMed Central 

Google Scholar 
21.Vatine, G., Vallone, D., Gothilf, Y. & Foulkes, N. S. It’s time to swim! Zebrafish and the circadian clock. FEBS Lett. 585, 1485–1494. https://doi.org/10.1016/j.febslet.2011.04.007 (2011).CAS 
Article 
PubMed 

Google Scholar 
22.Banaszak, A. T. & Lesser, M. P. Effects of solar ultraviolet radiation on coral reef organisms. Photochem. Photobiol. Sci. 8, 1276. https://doi.org/10.1039/b902763g (2009).CAS 
Article 
PubMed 

Google Scholar 
23.Häder, D.-P., Kumar, H. D., Smith, R. C. & Worrest, R. C. Effects of solar UV radiation on aquatic ecosystems and interactions with climate change. Photochem. Photobiol. Sci. 6, 267–285. https://doi.org/10.1039/B700020K (2007).Article 
PubMed 

Google Scholar 
24.Eckes, M., Siebeck, U., Dove, S. & Grutter, A. Ultraviolet sunscreens in reef fish mucus. Mar. Ecol. Prog. Ser. 353, 203–211. https://doi.org/10.3354/meps07210 (2008).CAS 
Article 
ADS 

Google Scholar 
25.Kienzler, A., Bony, S. & Devaux, A. DNA repair activity in fish and interest in ecotoxicology: A review. Aquat. Toxicol. 134–135, 47–56. https://doi.org/10.1016/j.aquatox.2013.03.005 (2013).CAS 
Article 
PubMed 

Google Scholar 
26.Hoogenboom, I., Daan, S., Dallinga, J. H. & Schoenmakers, M. Seasonal change in the daily timing of behaviour of the common vole, Microtus arvalis. Oecologia 61, 18–31. https://doi.org/10.1007/BF00379084 (1984).27.Tan, M. H. et al. Finding Nemo: Hybrid assembly with Oxford Nanopore and Illumina reads greatly improves the clownfish (Amphiprion ocellaris) genome assembly. GigaScience. https://doi.org/10.1093/gigascience/gix137 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
28.Cavallari, N. et al. A blind circadian clock in cavefish reveals that opsins mediate peripheral clock photoreception. PLoS Biol. https://doi.org/10.1371/journal.pbio.1001142 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
29.Vallone, D., Gondi, S. B., Whitmore, D. & Foulkes, N. S. E-box function in a period gene repressed by light. Proc. Natl. Acad. Sci. 101, 4106–4111. https://doi.org/10.1073/pnas.0305436101 (2004).CAS 
Article 
PubMed 
ADS 

Google Scholar 
30.Vatine, G. et al. Light directs Zebrafish period2 expression via conserved D and E boxes. PLOS Biol. https://doi.org/10.1371/journal.pbio.1000223 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
31.Mracek, P. et al. Regulation of per and cry Genes Reveals a Central Role for the D-Box Enhancer in Light-Dependent Gene Expression. PLOS ONE https://doi.org/10.1371/journal.pone.0051278 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
32.Zhao, H. et al. Modulation of DNA repair systems in blind cavefish during evolution in constant darkness. Curr. Biol. 28, 3229-3243.e4. https://doi.org/10.1016/j.cub.2018.08.039 (2018).CAS 
Article 
PubMed 

Google Scholar 
33.Tolimieri, N., Haine, O., Jeffs, A., McCauley, R. & Montgomery, J. Directional orientation of pomacentrid larvae to ambient reef sound. Coral Reefs 23, 184–191. https://doi.org/10.1007/s00338-004-0383-0 (2004).Article 

Google Scholar 
34.Fisher, R. & Bellwood, D. Undisturbed swimming behaviour and nocturnal activity of coral reef fish larvae. Mar. Ecol. Prog. Ser. 263, 177–188. https://doi.org/10.3354/meps263177 (2003).Article 
ADS 

Google Scholar 
35.Elliott, J. K. & Mariscal, R. N. Ontogenetic and interspecific variation in the protection of anemonefishes from sea anemones. J. Exp. Mar. Biol. Ecol. 208, 57–72. https://doi.org/10.1016/S0022-0981(96)02629-9 (1997).Article 

Google Scholar 
36.Fautin, D. G. The anemonefish symbiosis: What is known and what is not. Symbiosis 10, 23–46 (1991).
Google Scholar 
37.Di Rosa, V., Frigato, E., López-Olmeda, J. F., Sánchez-Vázquez, F. J. & Bertolucci, C. The light wavelength affects the ontogeny of clock gene expression and activity rhythms in zebrafish larvae. PLOS ONE https://doi.org/10.1371/journal.pone.0132235 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
38.Idda, M. L. et al. Chapter 3—Circadian clocks: Lessons from fish. In Kalsbeek, A., Merrow, M., Roenneberg, T. & Foster, R. G. (eds.) The Neurobiology of Circadian Timing, vol. 199 of Progress in Brain Research, 41–57, DOI: https://doi.org/10.1016/B978-0-444-59427-3.00003-4 (Elsevier, 2012).39.Patiño, M. A. L., Rodríguez-Illamola, A., Conde-Sieira, M., Soengas, J. L. & Míguez, J. M. Daily rhythmic expression patterns of Clock1a, Bmal1, and Per1 genes in retina and hypothalamus of the rainbow trout, Oncorhynchus Mykiss. Chronobiol. Int. 28, 381–389. https://doi.org/10.3109/07420528.2011.566398 (2011).CAS 
Article 
PubMed 

Google Scholar 
40.Vera, L. M. et al. Light and feeding entrainment of the molecular circadian clock in a marine teleost (Sparus aurata). Chronobiol. Int. 30, 649–661. https://doi.org/10.3109/07420528.2013.775143 (2013).CAS 
Article 
PubMed 

Google Scholar 
41.Martín-Robles, A. J., Whitmore, D., Sánchez-Vázquez, F. J., Pendón, C. & Muñoz-Cueto, J. A. Cloning, tissue expression pattern and daily rhythms of Period1, Period2, and Clock transcripts in the flatfish Senegalese sole,Solea senegalensis. J. Comp. Physiol. B 182, 673–685. https://doi.org/10.1007/s00360-012-0653-z (2012).CAS 
Article 
PubMed 

Google Scholar 
42.Park, J.-G., Park, Y.-J., Sugama, N., Kim, S.-J. & Takemura, A. Molecular cloning and daily variations of the Period gene in a reef fish Siganus guttatus. J. Comp. Physiol. A 193, 403–411. https://doi.org/10.1007/s00359-006-0194-6 (2007).CAS 
Article 

Google Scholar 
43.Martín-Robles, A. J., Isorna, E., Whitmore, D., Muñoz-Cueto, J. A. & Pendón, C. The clock gene Period3 in the nocturnal flatfish Solea senegalensis: Molecular cloning, tissue expression and daily rhythms in central areas. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 159, 7–15. https://doi.org/10.1016/j.cbpa.2011.01.015 (2011).CAS 
Article 
PubMed 

Google Scholar 
44.Whitmore, D., Foulkes, N. S., Strähle, U. & Sassone-Corsi, P. Zebrafish Clock rhythmic expression reveals independent peripheral circadian oscillators. Nat. Neurosci. 1, 701–707. https://doi.org/10.1038/3703 (1998).CAS 
Article 
PubMed 

Google Scholar 
45.Yamazaki, S. et al. Resetting central and peripheral circadian oscillators in transgenic rats. Science 288, 682–685. https://doi.org/10.1126/science.288.5466.682 (2000).CAS 
Article 
PubMed 
ADS 

Google Scholar 
46.Yagita, K. et al. Development of the circadian oscillator during differentiation of mouse embryonic stem cells in vitro. Proc. Natl. Acad. Sci. 107, 3846–3851. https://doi.org/10.1073/pnas.0913256107 (2010).Article 
PubMed 
ADS 

Google Scholar 
47.Challet, E. Minireview: Entrainment of the suprachiasmatic clockwork in diurnal and nocturnal mammals. Endocrinology 148, 5648–5655. https://doi.org/10.1210/en.2007-0804 (2007).CAS 
Article 
PubMed 

Google Scholar 
48.del Pozo, A., Montoya, A., Vera, L. M. & Sánchez-Vázquez, F. J. Daily rhythms of clock gene expression, glycaemia and digestive physiology in diurnal/nocturnal European seabass. Physiol. Behav. 106, 446–450. https://doi.org/10.1016/j.physbeh.2012.03.006 (2012).CAS 
Article 
PubMed 

Google Scholar 
49.Job, S. & Shand, J. Spectral sensitivity of larval and juvenile coral reef fishes: Implications for feeding in a variable light environment. Mar. Ecol. Prog. Ser. 214, 267–277. https://doi.org/10.3354/meps214267 (2001).Article 
ADS 

Google Scholar 
50.Buston, P. M. & García, M. B. An extraordinary life span estimate for the clown anemonefish Amphiprion percula. J. Fish Biol. 70, 1710–1719. https://doi.org/10.1111/j.1095-8649.2007.01445.x (2007).Article 

Google Scholar 
51.Godwin, J. & Fautin, D. G. Defense of host actinians by anemonefishes. Copeia 902–908, 1992. https://doi.org/10.2307/1446171 (1992).Article 

Google Scholar 
52.Roopin, M. & Chadwick, N. E. Benefits to host sea anemones from ammonia contributions of resident anemonefish. J. Exp. Mar. Biol. Ecol. 370, 27–34. https://doi.org/10.1016/j.jembe.2008.11.006 (2009).CAS 
Article 

Google Scholar 
53.Cleveland, A., Verde, E. A. & Lee, R. W. Nutritional exchange in a tropical tripartite symbiosis: Direct evidence for the transfer of nutrients from anemonefish to host anemone and zooxanthellae. Mar. Biol. 158, 589–602. https://doi.org/10.1007/s00227-010-1583-5 (2011).Article 

Google Scholar 
54.Verde, E. A., Cleveland, A. & Lee, R. W. Nutritional exchange in a tropical tripartite symbiosis II: Direct evidence for the transfer of nutrients from host anemone and zooxanthellae to anemonefish. Mar. Biol. 162, 2409–2429. https://doi.org/10.1007/s00227-015-2768-8 (2015).CAS 
Article 

Google Scholar 
55.da Silva, K. B. & Nedosyko, A. Sea Anemones and Anemonefish: A Match Made in Heaven. In Goffredo, S. & Dubinsky, Z. (eds.) The Cnidaria, Past, Present and Future, 425–438 (Springer International Publishing, 2016). https://doi.org/10.1007/978-3-319-31305-4_27.56.Vallone, D., Santoriello, C., Gondi, S. B. & Foulkes, N. S. Basic protocols for zebrafish cell lines. In Rosato, E. (ed.) Circadian Rhythms: Methods and Protocols, 429–441. https://doi.org/10.1007/978-1-59745-257-1_35 (Humana Press, 2007).57.Dekens, M. P. S., Foulkes, N. S. & Tessmar-Raible, K. Instrument design and protocol for the study of light controlled processes in aquatic organisms, and its application to examine the effect of infrared light on zebrafish. PLoS ONE 12. https://doi.org/10.1371/journal.pone.0172038 (2017).58.Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675. https://doi.org/10.1038/nmeth.2089 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
59.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2020).60.Wickham, H. Ggplot2: Elegant Graphics for Data Analysis. Use R! (Springer, 2009).61.Thaben, P. F. & Westermark, P. O. Detecting rhythms in time series with RAIN. J. Biol. Rhythm. 29, 391–400. https://doi.org/10.1177/0748730414553029 (2014).Article 

Google Scholar 
62.Kõressaar, T. et al. Primer3_masker: Integrating masking of template sequence with primer design software. Bioinformatics 34, 1937–1938. https://doi.org/10.1093/bioinformatics/bty036 (2018).CAS 
Article 
PubMed 

Google Scholar 
63.Untergasser, A. et al. Primer3—new capabilities and interfaces. Nucleic Acids Res. 40, e115. https://doi.org/10.1093/nar/gks596 (2012).64.Kõressaar, T. & Remm, M. Enhancements and modifications of primer design program Primer3. Bioinformatics 23, 1289–1291. https://doi.org/10.1093/bioinformatics/btm091 (2007).CAS 
Article 
PubMed 

Google Scholar 
65.Ye, J. et al. Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction. BMC Bioinforma 13, 134. https://doi.org/10.1186/1471-2105-13-134 (2012).CAS 
Article 

Google Scholar 
66.Wit, P. D. et al. The simple fool’s guide to population genomics via RNA-Seq: An introduction to high-throughput sequencing data analysis. Mol. Ecol. Resour. 12, 1058–1067. https://doi.org/10.1111/1755-0998.12003 (2012).CAS 
Article 
PubMed 

Google Scholar  More

Collection of crabsMale snow crabs of legal size and with hard shells were caught by the commercial SC vessel Northeastern (Opilio AS) using traditional SC pots in the NEAFC area (N 75° 49.2 E 37° 39.2). SCs were stored live onboard the vessel and subsequently delivered to Nofima’s facilities in Tromsø (N 69° 39). The crabs were caught in June, and September 2016, February, April, and December 2017 and will only be referred to by month. Upon slaughtering, data from individual crabs was obtained by recording the weight of the whole animal, clusters + claws, hemolymph, hepatopancreas, and gills (n = 56 September, n = 45 December, n = 29 February, n = 66 April, n = 50 June). Subsequently, different fractions were pooled and analysed as outlined below.Biochemical- and meat content-analysesThe biochemical analyses were performed on each month of analysis (September, December, February, April, and June) and consisted of water, protein, lipid, and ash contents. All analyses were performed on meat (i.e., main product) and the different co-products divided in the following ways: pooled internal organs (mainly hemolymph, hepatopancreas and gonads) with and without added carapace, hemolymph alone and hepatopancreas alone. Lipid class and fatty acid analyses were performed on the lipid storage organ hepatopancreas. Each biochemical analysis consisted of co-products from 10 randomly selected animals. All biochemical analyses (water-, ash-, lipid and protein-content) were determined by Toslab (9266 Tromsø, Norway), lipid classes and fatty acid identifications were performed by Biolab (5141 Fyllingsdalen, Norway). Both are commercial laboratories accredited according to ISO 17025.Water and dry matter content3–5 g of material was weighed in a marked porcelain crucible. The crucible was placed in a preheated drying cabinet at 103 °C ± 1 °C. After precisely 4 h 30 min, the crucible was allowed to cool down in a desiccator before being weighed. Water and dry matter contents were calculated according to Eqs. (1) and (2) respectively:$$Waterleft(%right)=frac{left(a-bright)}{w}times 100$$
(1)
$$Dry;matter;content left(%right)=frac{left(b-cright)}{w}times 100$$
(2)
where a = weight (g) of crucible with weighed sample; b = weight (g) of crucible with dried sample; c = weight (g) of crucible; w = weight (g) of weighed sample9.Ash content3–5 g of material was weighed in a marked porcelain crucible. The crucible was placed in a preheated muffle furnace at 550 °C ± 20 °C. After 16 h, the crucible was allowed to cool down in a desiccator before being weighed. The ash content was calculated according to Eq. (3):$$Ash left(%right)=frac{left(d-cright)}{{w}^{^{prime}}}times 100$$
(3)
where d = weight (g) of crucible with calcinated sample; c = weight (g) of crucible; w′ =  weight (g) of dry matter sample10.Fat contentThe fat in the samples was extracted with a polar solvent consisting of CHCl3, MeOH and H2O in a mixing ratio of 1:2:0.8 to give a single-phase system. 5–20 g of material was weighed into a 250 ml test tube. H2O was added so that water content plus added material corresponded to 16 ml. MeOH (40 ml) and CHCl3 (20 ml) were added. The mix was homogenized for 60 s. CHCl3 (20 ml) was again added and the mix was homogenized for 30 s H2O (20 ml) was added, and the mix was homogenized again for 30 s. The test tube was sealed and cooled in a water bath with ice. The emulsion was quickly filtered out through a small cotton ball in a funnel. The upper layer of the collected liquid consisting of MeOH and H2O was removed by suction. 5–20 ml of the remaining CHCl3 phase was transferred to a tared evaporation dish with a positive displacement pipette. The solvent was evaporated with an infrared lamp. The dish was cooled in a desiccator and weighed. The fat content was calculated according to the Eq. (4):$$Fat;content left(%right)=frac{dtimes b}{W times left(c-frac{d}{mathrm{0,92}}right)}times 100$$
(4)
where b = ml CHCl3 added; c = ml CHCl3 transferred; d = weight of fat in evaporation dish (g); 0.92 = specific gravity for fat, g/ml; w = weight (g) of the sample11.Protein contentProtein content analysis was performed with a fully automated Kjeltec 8400 (Foss Analytics, Denmark). 0.5–1 g of nitrogen free paper of previously dried sample was allowed to be digested in a digestion unit with concentrated H2SO4 (17.5 ml) and two catalyser tablets for 2 h 20 min at 420 °C. The digested liquid was transferred to the titration unit after cooling and was titrated fully automated by the equipment.Blanking was performed only with nitrogen free paper, titration with standardized HCl solution and 1% (w/w) boric acid solution containing a pH sensitive indicator. The protein content was calculated according to the Eq. (5):$$Protein;content left(%right)=frac{mathrm{14,007}times N times f times left(a-bright)}{wtimes 1000}times 100$$
(5)
where 14.007 = atomic weight of Nitrogen; N = Normality of the titration solution; f = protein factor (6.25); w = weight (g) of weighed sample; a = ml of HCl consumed for sample titration; b = ml of HCl consumed for blank titration12.
Cis-fatty acid and trans-fatty acids compositionThis method was designed to determine the fatty acid composition of marine oils and marine oil esters in relative (area-%) values, and eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in absolute (g/100 g) values using a bonded polyglycol liquid phase in a flexible fused silica capillary column. C23:0 fatty acid was used as an internal standard.For methyl esterification of oil samples for the analysis of cis-fatty acids, two drops of the oil sample were weighed and transferred to a 15 ml test tube with a screw cap. The test amount should be between 20 and 35 mg. Exactly 900 µl of the internal standard solution was added. The solvent was evaporated by nitrogen on a heating block at 80 °C. NaOH solution (1.5 ml, 0.5 N) was added. The mix was incubated in boiling water for 5 min and cooled in cold water. A 15% BF3-solution (2 ml) was added. The mix was again incubated in boiling water for 30 min and cooled to 30–40 °C. Isooctane (1 ml) was added. A cork was set, and the mixture grated with gentle movements for 30 s. Saturated NaCl (5 ml) was added immediately. A cork was set, and the mixture was again grated with gentle movements for 30 s. The isooctane phase was transferred to a test tube with a lid. The test tube was centrifuged at 3000 rpm if phase separation was difficult to achieve. Another 1 ml of isooctane was added to the test tube. A cork was set, and the mixture grated with gentle movements for 30 s. The isooctane phase was transferred to the same test tube with a lid. 5 µl of this transferred isooctane phase was diluted into a new test tube with 1 ml of isooctane.The procedure for methyl esterification of trans-fatty acids was identical to that for methyl esterification of cis-fatty acids, with one exception: incubation time after the addition of BF3-solution was 5 min.For the GC analysis an analytical capillary column (60 m × 0.25 mm × 0.25 µm-70% Cyanopropyl Polysilphenylene-siloxane) was used. (P/N: 054623, manufacturer: SGE). During the analysis the gas valves on the wall panel for synthetic air and hydrogen were left open.Two different GC programs were used for the analysis (Table 1).Table 1 GC-program for analysis of fatty acids.Full size tableThe identification of the different fatty acid methyl esters was performed by comparing the pattern and relative retention times by chromatography of different standards. Empirical response factor was used in quantifying fatty acids, based on calibration solution analysis with equal amounts of included fatty acid methyl esters (GLC-793, Nu-Chek-Prep Inc. Elysian MN, USA). It was calculated according to the Eq. (6):$${RF}_{em }=frac{{A}_{23:0}}{{A}_{FS}}$$
(6)
The absolute amount of each fatty acid, calculated as fatty acid methyl ester was calculated according to the Eq. (7):$${C}_{FS}(g/100)=left(frac{{A}_{FS }times {IS}_{W} times {RF}_{em} }{ {A}_{23:0} times W}right)times 100$$
(7)
where AFS = Area of the fatty acid A23:0 = Area of internal standard; ISW = Number of milligrams (mg) internal standard added; RFem = Empirical response factor to the fatty acid with reference to 23:0; W = Weighed sample amount in milligrams (mg); 100 = Factor for conversion to g/100 g13,14,15.Lipid classesThe dominant lipid classes were separated by HPLC equipped with a LiChroCART 125-4, diol 5 µm column and a Charged Aerosol Detector (CAD), using a tertiary gradient mobile phase composition. The fat was extracted as previously described (“Fat content”). A suitable amount of CH3Cl was added to the fat sample, the mix was pipetted into a tared test tube and evaporated on a heating block under nitrogen. The temperature of the heating block must be at 60 °C. The test tube with the evaporated sample was weighed and the weight of the fat calculated. The sample was diluted with an appropriate amount of CH3Cl. Prior to injection the CAD detector was programmed with these settings: range = 500, Filter = Med, Offset = 5, T = 30 °C. The gradient profile is shown Table 2.Table 2 Gradient profile for separation of lipid classes.Full size tableThe quantification was based on external standards with a purity ≥ 98%. Triacylglycerols (TAG) in natural marine oils have a large elution range compared to the other lipid classes, therefore a standard control oil (fish oil) for the preparation of the TAG standard curve was used. This provides a better adaptation to real samples compared to a pure TAG compound.Meat contentMeat content was measured on cooked clusters from the middle of the merus on the first walking leg as an area-percentage of meat-to-shell using an elliptic area formula; Internal height and width of shells and external height and width of muscle was measured, width (w) and height (h) were multiplied to each other and π to calculate the elliptical areas (n = 56 September, n = 45 December, n = 29 February, n = 66 April, n = 50 June). The meat content (MC) was defined as the percentage of space occupied by meat according to the Eq. (8) and the hepatopancreas index (HI) was calculated according to the Eq. (9):$$MCleft(%right)=frac{h;muscletimes w;muscletimes pi }{h;shelltimes w;shelltimes pi }times 100$$
(8)
$$HIleft(%right)=frac{{W}_{hept}}{{W}_{live}}times 100$$
(9)
where Whept is the weight of the hepatopancreas and Wlive is the live weight of the crab (n = 56 September, n = 50 December, n = 70 February, n = 70 April, n = 50 June).Graphs, ordinary one-way ANOVA, and linear regressions were made using GraphPad Prism version 7.03 (CA, USA). Meat content data failed the normality test (Shapiro–Wilk) and was analysed using Kruskal–Wallis One Way Analyses of Variance on Ranks and statistical significance was assumed when P  More