Philippa Kaur

Higham, T. E. et al. Linking ecomechanical models and functional traits to understand phenotypic diversity. Trends Ecol. Evol. 36, 860–873 (2021).Article 
PubMed 

Google Scholar 
Kearney, M. R., Jusup, M., McGeoch, M. A., Kooijman, S. A. & Chown, S. L. Where do functional traits come from? The role of theory and models. Funct. Ecol. 35, 1385–1396 (2021).Article 
CAS 

Google Scholar 
Mayr, E. Geographical character gradients and climatic adaptation. Evolution 10, 105–108 (1956).Article 

Google Scholar 
Gaston, K. J., Chown, S. L. & Evans, K. L. Ecogeographical rules: elements of a synthesis. J. Biogeogr. 35, 483–500 (2008).Article 

Google Scholar 
Chown, S. L. & Gaston, K. J. Macrophysiology for a changing world. Proc. R. Soc. B 275, 1469–1478 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
Rubalcaba, J. G. & Jimeno, B. Biophysical models unravel associations between glucocorticoids and thermoregulatory costs across avian species. Funct. Ecol. 36, 64–72 (2022).Article 
CAS 

Google Scholar 
Anderson, R. O., White, C. R., Chapple, D. G. & Kearney, M. R. A hierarchical approach to understanding physiological associations with climate. Glob. Ecol. Biogeogr. 31, 332–346 (2022).Article 

Google Scholar 
Angilletta, M. J. Jr, Niewiarowski, P. H. & Navas, C. A. The evolution of thermal physiology in ectotherms. J. Therm. Biol. 27, 249–268 (2002).Article 

Google Scholar 
Olalla‐Tárraga, M. Á., Rodríguez, M. Á. & Hawkins, B. A. Broad‐scale patterns of body size in squamate reptiles of Europe and North America. J. Biogeogr. 33, 781–793 (2006).Article 

Google Scholar 
Amado, T., Moreno Pinto, M. G. & Olalla‐Tárraga, M. Á. Anuran 3D models reveal the relationship between surface area‐to‐volume ratio and climate. J. Biogeogr. 46, 1429–1437 (2019).
Google Scholar 
Castro, K. M. S. A. et al. Water constraints drive allometric patterns in the body shape of tree frogs. Sci. Rep. 11, 1218 (2021).Clusella-Trullas, S., Terblanche, J. S., Blackburn, T. M. & Chown, S. L. Testing the thermal melanism hypothesis: a macrophysiological approach. Funct. Ecol. 22, 232–238 (2008).Ghalambor, C. K., Huey, R. B., Martin, P. R., Tewksbury, J. J. & Wang, G. Are mountain passes higher in the tropics? Janzen’s hypothesis revisited. Integr. Comp. Biol. 46, 5–17 (2006).Article 
PubMed 

Google Scholar 
Bennett, J. M. et al. The evolution of critical thermal limits of life on Earth. Nat. Commun. 12, 1198 (2021).Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl Acad. Sci. USA 111, 5610–5615 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Muñoz, M. M. The Bogert effect, a factor in evolution. Evolution 76, 49–66 (2021).Article 
PubMed 

Google Scholar 
Bogert, C. M. Thermoregulation in reptiles, a factor in evolution. Evolution 3, 195–211 (1949).Article 
CAS 
PubMed 

Google Scholar 
Huey, R. B., Hertz, P. E. & Sinervo, B. Behavioral drive versus behavioral inertia in evolution: a null model approach. Am. Nat. 161, 357–366 (2003).Article 
PubMed 

Google Scholar 
Kearney, M. R. & Porter, W. P. NicheMapR—an R package for biophysical modelling: the microclimate model. Ecography 40, 664–674 (2017).Article 

Google Scholar 
Messier, J., McGill, B. J., Enquist, B. J. & Lechowicz, M. J. Trait variation and integration across scales: is the leaf economic spectrum present at local scales? Ecography 40, 685–697 (2017).Article 

Google Scholar 
Ricklefs, R. E. & Schluter, D. (eds) Species Diversity in Ecological Communities: Historical and Geographical Perspectives (Univ. Chicago Press, 1993).Angilletta, M. J. Jr, Steury, T. D. & Sears, M. W. Temperature, growth rate, and body size in ectotherms: fitting pieces of a life-history puzzle. Integr. Comp. Biol. 44, 498–509 (2004).Article 
PubMed 

Google Scholar 
Pincheira-Donoso, D. The balance between predictions and evidence and the search for universal macroecological patterns: taking Bergmann’s rule back to its endothermic origin. Theory Biosci. 129, 247–253 (2010).Article 
PubMed 

Google Scholar 
Slavenko, A. et al. Global patterns of body size evolution in squamate reptiles are not driven by climate. Glob. Ecol. Biogeogr. 28, 471–483 (2019).Article 

Google Scholar 
Stevenson, R. D. Body size and limits to the daily range of body temperature in terrestrial ectotherms. Am. Nat. 125, 102–117 (1985).Article 

Google Scholar 
Rubalcaba, J. G., Gouveia, S. F. & Olalla‐Tárraga, M. A. A mechanistic model to scale up biophysical processes into geographical size gradients in ectotherms. Glob. Ecol. Biogeogr. 28, 793–803 (2019).Article 

Google Scholar 
Rubalcaba, J. G. & Olalla‐Tárraga, M. Á. The biogeography of thermal risk for terrestrial ectotherms: scaling of thermal tolerance with body size and latitude. J. Anim. Ecol. 89, 1277–1285 (2020).Article 
PubMed 

Google Scholar 
Pincheira-Donoso, D., Hodgson, D. J. & Tregenza, T. The evolution of body size under environmental gradients in ectotherms: why should Bergmann’s rule apply to lizards? BMC Evol. Biol. 8, 68 (2008).Jablonski, D. Biotic interactions and macroevolution: extensions and mismatches across scales and levels. Evolution 62, 715–739 (2008).Article 
PubMed 

Google Scholar 
Kearney, M. R., Porter, W. P. & Huey, R. B. Modelling the joint effects of body size and microclimate on heat budgets and foraging opportunities of ectotherms. Methods Ecol. Evol. 12, 458–467 (2021).Article 

Google Scholar 
Campbell-Staton, S. C., Bare, A., Losos, J. B., Edwards, S. V. & Cheviron, Z. A. Physiological and regulatory underpinnings of geographic variation in reptilian cold tolerance across a latitudinal cline. Mol. Ecol. 27, 2243–2255 (2018).Article 
CAS 
PubMed 

Google Scholar 
Boretto, J. M., Fernández, J. B., Cabezas-Cartes, F., Medina, M. S. & Ibargüengoytía, N. R. in Lizards of Patagonia (eds Morando, M. & Avila, L. J.) 335–371 (Springer, 2020).Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Araújo, M. B. et al. Heat freezes niche evolution. Ecol. Lett. 16, 1206–1219 (2013).Article 
PubMed 

Google Scholar 
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Global analysis of thermal tolerance and latitude in ectotherms. Proc. R. Soc. B 278, 1823–1830 (2011).Article 
PubMed 

Google Scholar 
Hoffmann, A. A., Chown, S. L. & Clusella‐Trullas, S. Upper thermal limits in terrestrial ectotherms: how constrained are they? Funct. Ecol. 27, 934–949 (2013).Article 

Google Scholar 
Sunday, J. et al. Thermal tolerance patterns across latitude and elevation. Philos. Trans. R. Soc. B 374, 20190036 (2019).Article 

Google Scholar 
Huey, R. B. & Slatkin, M. Cost and benefits of lizard thermoregulation. Q. Rev. Biol. 51, 363–384 (1976).Article 
CAS 
PubMed 

Google Scholar 
Vasseur, D. A. et al. Increased temperature variation poses a greater risk to species than climate warming. Proc. R. Soc. B 281, 20132612 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Porter, W. P., Mitchell, J. W., Beckman, W. A. & DeWitt, C. B. Behavioral implications of mechanistic ecology. Oecologia 13, 1–54 (1973).Article 
CAS 
PubMed 

Google Scholar 
Hertz, P. E., Huey, R. B. & Stevenson, R. D. Evaluating temperature regulation by field-active ectotherms: the fallacy of the inappropriate question. Am. Nat. 142, 796–818 (1993).Article 
CAS 
PubMed 

Google Scholar 
Fey, S. B. et al. Opportunities for behavioral rescue under rapid environmental change. Glob. Change Biol. 25, 3110–3120 (2019).Article 

Google Scholar 
Martin, T. L. & Huey, R. B. Why ‘suboptimal’ is optimal: Jensen’s inequality and ectotherm thermal preferences. Am. Nat. 171, E102–E118 (2008).Article 
PubMed 

Google Scholar 
R Core Team. A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).Campbell, G. S. & Norman, J. M. An Introduction to Environmental Biophysics 2nd edn (Springer-Verlag, 1998).Mao, J. & Yan, B. Global Monthly Mean Leaf Area Index Climatology, 1981–2015 (ORNL DAAC, 2019).Meiri, S. et al. Are lizards feeling the heat? A tale of ecology and evolution under two temperatures. Glob. Ecol. Biogeogr. 22, 834–845 (2013).Article 

Google Scholar 
Marino, S., Hogue, I. B., Ray, C. J. & Kirschner, D. E. A methodology for performing global uncertainty and sensitivity analysis in systems biology. J. Theor. Biol. 254, 178–196 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
Renardy, M., Hult, C., Evans, S., Linderman, J. J. & Kirschner, D. E. Global sensitivity analysis of biological multiscale models. Curr. Opin. Biomed. Eng. 11, 109–116 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Carnell, R. lhs: Latin hypercube samples. R package version 1.1.1 (2020).Meiri, S. Traits of lizards of the world: variation around a successful evolutionary design. Glob. Ecol. Biogeogr. 27, 1168–1172 (2018).Article 

Google Scholar 
Clusella-Trullas, S., Blackburn, T. M. & Chown, S. L. Climatic predictors of temperature performance curve parameters in ectotherms imply complex responses to climate change. Am. Nat. 177, 738–751 (2011).Article 
PubMed 

Google Scholar 
Bennett, J. M. et al. GlobTherm, a global database on thermal tolerances for aquatic and terrestrial organisms. Sci. Data 5, 180022 (2018).Roll, U. et al. The global distribution of tetrapods reveals a need for targeted reptile conservation. Nat. Ecol. Evol. 1, 1677–1682 (2017).Article 
PubMed 

Google Scholar 
Tonini, J. F. R., Beard, K. H., Ferreira, R. B., Jetz, W. & Pyron, R. A. Fully-sampled phylogenies of squamates reveal evolutionary patterns in threat status. Biol. Conserv. 204, 23–31 (2016).Article 

Google Scholar 
Ives, A. R. R2s for correlated data: phylogenetic models, LMMs, and GLLMs. Syst. Biol. 68, 234–251 (2019).Article 
PubMed 

Google Scholar 
Johnson, T. F., Isaac, N. J. B., Paviolo, A. & González-Suárez, M. Handling missing values in trait data. Glob. Ecol. Biogeogr. 30, 51–62 (2020).Article 

Google Scholar 
Goolsby, E. W., Bruggeman, J. & Ané, C. Rphylopars: fast multivariate phylogenetic comparative methods for missing data and within‐species variation. Methods Ecol. Evol. 8, 22–27 (2017).Article 

Google Scholar 
Koenker, R. et al. Package ‘quantreg’ (R-CRAN, 2018); https://cran.r-project.org/web/packages/quantreg/quantreg.pdfGriffith, D. A. & Peres-Neto, P. R. Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses. Ecology 87, 2603–2613 (2006).Article 
PubMed 

Google Scholar 
Bivand, R. R packages for analyzing spatial data: a comparative case study with areal data. Geogr. Anal. 54, 488–518 (2022).Article 

Google Scholar 
Rubalcaba, J. G. et al. Data: ‘Climate drives global functional trait variation in lizards’. figshare https://doi.org/10.6084/m9.figshare.19949315 (2022). More

We investigated the complex relationships between the environmental characteristics of blue spaces and visit-related mental well-being in a multi-country study including 17 bluespace types and four facets of subjective well-being. Our aim was to operationalise, and consider the utility of, the Bratman et al.9 conceptual model that links ecosystem services (ESS) with mental health.Consistent with the proposed conceptual model, mental well-being outcomes relied on a complex interplay of individual, environmental, and visit characteristics.Summary of findingsOverall, bluespace visits were associated with better subjective mental well-being outcomes if the visits took place in nearby coastal areas or rural rivers, were perceived as safe and to have good water quality, and had a long duration. They could involve a range of activities such as playing with children, socialising, or walking. The degree to which the perceived presence of wildlife predicted visit satisfaction varied depending on the bluespace type, suggesting that the importance of ecosystem features such as biodiversity may vary by the setting.We can also identify the combination of environmental and visit characteristics associated with particularly high levels of well-being for a specific outcome. For example, an optimal visit in terms of happiness might be to sandy beaches where there are high levels of perceived safety and excellent water quality; with a visit lasting at least three hours; and possibly involving playing with children, socialising, sunbathing/paddling and/or walking with a dog; and has short travel times that do not involve public transport.RQ1—natural and environmental featuresResearch question 1a—Which bluespace type(s) were associated with the highest levels of recalled visit mental well-being?Four of the five bluespace types associated with the highest levels of visit satisfaction were coastal (sea cliffs, rocky shore, sandy beaches, rural river and seaside promenade), indicating that these environments may be particularly beneficial for well-being. Visits to these environments were also associated with the lowest levels of visit anxiety, with the exception of seaside promenade and sea cliffs, which were not significantly different to the grand mean. Seaside promenade was the only urban environment in the top five.In addition, only coastal sites were associated with significantly higher levels of visit happiness (compared to the grand mean), further highlighting the potential importance of these environments. Although not explored here, coastal scenes tend to be associated with particularly high aesthetic and scenic value25,26 which may also be positively related to subjective well-being.These findings are broadly consistent with other studies from the UK17,27, but are extended here to our international sample. White et al.28 also used data from the BlueHealth International Survey (BIS) and explored visit frequency to different environments and associations with general mental health and well-being outcomes, including the World Health Organisation five-item Well-being index referring to the two weeks prior to the survey. Consistent with the results here, they found that visit frequency to “coastal blue” environments was more strongly associated with psychological well-being in general than visit frequency to “inland blue” environments. Our study adds to these more general findings by showing that these associations may come as a direct result of the recalled well-being experienced on specific visits to these locations.Confidence in our results was strengthened as we included general mental well-being in our analysis to adjust for whether happier people tend to visit sandy beaches, for example. The results for visit anxiety were not always the inverse of the trends observed in the positive measures of well-being, supporting the need to look at multiple aspects of mental well-being when considering the effects of nature contact.Research question 1b—Which bluespace qualities were associated with the highest levels of recalled visit mental well-being?Of the range of qualities that we investigated as predictors, perceived safety and ‘excellent’ water quality (vs. ‘sufficient’) consistently exhibited the strongest relationships with subjective mental well-being. Perceived safety has been found to be important when visiting blue spaces in several qualitative studies29,30,31, as well as a quantitative study with older adults in Hong Kong14. Blue spaces have particular safety issues with respect to drowning32,33, but fear of crime29,30,33 or pedestrian safety34 may also be relevant.Water quality has also been found to be important in previous economic valuation studies of recreational use and enjoyment of lakes and estuaries in the USA and Australia35,36 as well as a contingent behaviour experiment carried out as part of the BlueHealth International Survey (in European countries only)37. We recognise that here we used a metric of perceived water quality, rather than measures based on biological or toxicological sampling. Nevertheless, perceptions have been reported to positively correlate with sampled water quality parameters38, although assessments can vary systematically such as by bluespace type39. Highly visible harmful algal blooms, for instance, have also been found to affect experiences of blue spaces40.Further, and again consistent with earlier work15,41,42, the presence of facilities and wildlife, and absence of litter, were generally associated with better subjective mental well-being. Both perceived presence of wildlife and facilities were also associated with higher levels of anxiety however, indicating complexities between environmental qualities and well-being. Some wildlife may be deemed unpleasant or an ecosystem disservice, for example. The presence of good facilities may indicate the presence of more people; and visitor density in natural environments can be related to preference43. These results highlight the importance of environmental quality and not just type, consistent with other frameworks12,37.Research question 2—How is exposure, as operationalised by visit duration, related to recalled visit mental well-being?Broadly consistent with research in the green and bluespace literature14,17,44, we found that mental well-being outcomes were generally higher with greater exposure as indicated by visit duration. For decreasing visit anxiety, this was only significant when visits were longer than an hour and a half. As we did not measure pre-visit anxiety levels, we are cautious about identifying this as a potential temporal threshold for reducing anxiety at this stage.Similarly, also using the BlueHealth International Survey, White et al.28 found that well-being outcomes were higher with greater visit exposure to green and blue spaces using a metric of visit frequency. However, in contrast to this and other research which looked at overall weekly aggregated time in nature (e.g.28,45), we have no evidence of diminishing marginal returns as the effect sizes associated with specific visit duration continued to increase with increasing duration.Research question 3—What experiences in blue spaces, in terms of activities (3a) and companions (3b), are associated with the most positive recalled visit mental well-being outcomes?Although walking was the most popular activity, the activity with the highest mental well-being ratings was playing with children, especially in certain locations such as beaches (Fig. 4). However, we also find that anxiety tended to be higher when children were present. We speculate that the purpose of the visit may be important. For example, many who go to the beach with children do so in order to play. However, if children are present on more adult-oriented activities such as hiking, this may increase adult anxiety during the visit. From a representative sample of English adults, White et al.17 found that recent nature visits with children were associated with the lowest levels of well-being. Therefore, visits with children may be associated with a more complex set of emotions, being both slightly more stressful, but also potentially more rewarding and ‘meaningful’46. Ecosystem features of beaches may be particularly supportive of high well-being activities. A qualitative study in the UK, for instance, highlighted the particular opportunities for adults and children to play together at the beach, including rock-pooling and making sandcastles as well as water-based activities47.Visits with other adults were associated with higher levels of both visit satisfaction and worthwhile-ness, and socialising as an activity was associated with better visit well-being for all outcomes compared to the grand mean. This is consistent with studies using the day reconstruction method, which link activities with experiential well-being, in the USA48 and Germany49 where socialising was associated with the highest, or second highest, levels of well-being for all the activities assessed. Further, social interactions have been recognised as an important benefit by many of those visiting freshwater blue spaces in a previous study18.Research question 4—Does the relationship between wildlife presence and recalled visit well-being vary by bluespace settings?The relationship between the presence of wildlife and visit satisfaction varied with bluespace type. The strongest positive association was found for fen, marsh and bog areas, which may also be related to the purpose of visit. For instance, those who visit places such as fens, marshes and bogs, may do so for the explicit purpose of observing wildlife (often birds) and the presence of wildlife would therefore be important for satisfaction with the visit.Perceptions towards wildlife have been found to vary by location in other studies. For example, in Sweden, greater prior experience with geese at beaches was associated with a negative attitude towards geese50. Further, the species present are likely to vary across different environments. In three urban areas in the UK, green spaces correlated with the abundance and species richness of birds considered to provide cultural services (songbirds and woodpeckers), while an abundance of birds considered to provide disservices (e.g. some gull species, feral pigeons) was independent of green spaces51. Preferences for some species over others may explain some of the negative or null relationships between the presence of wildlife at different blue spaces. These examples from the literature, alongside our own results, indicate the potential for benefits from the management of wildlife for psychological ecosystem services differentially across environments, although these should be considered alongside other conservation and ESS goals.MechanismsSeveral mechanisms potentially explain the beneficial effects of visiting blue spaces on mental health and well-being12, including the provision of opportunities for physical activity52,53; social interaction18; cognitive restoration and stress reduction17,54; emotion regulation55 and connecting with nature12. Consistent with these mechanisms, we found that respondents were using blue spaces for both physical activity and social interaction; and that playing with children and socialising were associated with particularly high levels of well-being.In addition to the positive association we find between some ESS and well-being, including presence of wildlife and water quality, additional bluespace ESS not considered here, may also affect mental health and well-being12. For example, the provision of a cooling effect56 and air pollution mitigation57.Strengths and limitationsA key strength is our operationalisation of the Bratman et al.9 conceptual model for mental health using data from a large, 18 country survey that included 17 different bluespace types, five quality metrics and four subjective mental well-being outcomes. The relatively high explanatory power of our models suggests all the variables we explored were important for subjective well-being.Despite the strengths, however, there were also several limitations. The survey was cross-sectional and causality cannot be inferred. For example, happier people may choose to visit a beach rather than another location, although we also controlled for general levels of subjective mental well-being in an attempt to control for this possibility (See Supplemental Materials). Further, although the majority of respondents (53%) recalled a visit within the last 7 days, some were recalling visits up to a month ago, with potential memory biases increasing in line with length of recall.Although our data were collected by an international market research company to be representative of age, gender and region within country, our online sample may not be fully representative across more characteristics and any country-level conclusions need to be treated with caution. We also acknowledge that there were no results from Africa, the Middle East or South America; and Hong Kong was the only representative from Asia. This suggests far more research is needed in other regions to better understand how bluespace ecosystems interact with subjective well-being globally.There may also be socioeconomic confounds that we did not include in our models which may account for some of the effects. Not everyone visits nature for recreation58, including about 4000 people here who did not visit a bluespace in the four weeks prior to responding to the survey. Some groups may therefore have been under-represented; and we should be careful in assuming that our findings generalise to all sub-population groups.Nevertheless, our visit sub-sample distributions were generally similar to that of the weighted percentages in the full sample, with the exception of age where those aged over 60 were under-represented (Table S2); therefore, we suspect these issues were not too influential for the overall results, although care needs to be extended to inferences with respect to older adults.A further limitation was that we only considered the qualities of places where people reported making recreational visits, with respondents presumably less likely to visit places where they feel really unsafe or lacking in facilities29. Further research may want to study responses to a broader range of bluespace settings, including those that are less visited, to determine the generalisability of the generally positive results found here. Such studies could use pre-existing tools to objectively assess the quality of blue spaces59.ImplicationsOur finding that coastal environments are particularly beneficial adds to the body of evidence linking coastal environments with health and well-being and suggests this is consistent across many countries. Previous research has found that greater proximity to blue spaces, especially coastal settings, predicts visit frequency14,60,61 as well as other health outcomes—e.g. reduced risk of mortality and better general health, well-being and physical activity53,62. Here, we found that shorter travel times also predict visit well-being, highlighting the importance of having equitable access to good quality natural environments near to people’s homes.We also identified that different types of coastal and inland blue spaces (e.g. seaside promande, rural rivers), with different qualities (e.g. wildlife present), involving particular types of activities in specific social configurations (e.g. playing with children), were especially good at promoting well-being. This moves beyond a simple location-based assessment of benefit to one that recognises the complex interplay between location, behavioural and social processes. Numerous commentators63 (including Bratman et al.9 on which we have based this paper) have argued that we need to go beyond the determinate effects of green and blue spaces and develop a far richer, more nuanced understanding. The approach we have taken here is intended as a step in this direction.In terms of policy applications, these results provide support for the potential health benefits of efforts to improve equitable access to high quality environments, such as the English Coast Path (https://englandcoastpath.co.uk/) and the creation of beaches in Barcelona with the Olympic project in 199264. Our results also hint at the importance of high-level legislation, such as the EU’s Bathing Waters Directive65 for mental well-being37. If conducted on a more fine-grained geographical level, results could have the potential to leverage public support for more localised conservation initiatives. Furthermore, such results could be used as a basis for integration into more systematic conservation planning66.Further researchAlthough we incorporate a range of variables in our analysis, and our pseudo-R2 values are relatively high for a social research context, considerable variation remains unexplained. Although other individual characteristics may be important, such as nature connectedness67 and memories68, further research could explore the specific ecosystem features and social contexts associated with the particular positive results from coastal spaces, which would be of interest to policy makers and environmental managers. We also speculated that purpose of visit may explain some of our findings. Further research could explore the interactions between motivations and location, experience, and well-being outcomes.The presence of wildlife was differentially important across bluespace types and further research could unpack this. Exploring similar possibilities for the other quality metrics, as well as considering additional ecosystem characteristics, would also be informative. For example, identifying which factors are important in perceptions of safety in blue spaces. Bratman et al.9 also considered effect modification by visitor characteristics and further research could include interactions, or sub-group analysis, by socio-demographic factors.Further research could also explore longer-term benefits of these features over repeated visits; the potential for ecosystem disservices, such as the relationships we find between an interaction of wildlife and ice rinks and well-being; the potential for negative outcomes associated with ecosystem degradation69; and the potential for positive mental health outcomes from ecological restoration70.We have demonstrated some of the complexities involved in the human-nature relationship and that many factors are related to the outcome from a visit. The conceptual model applied allows the investigation of a wide range of variables including natural features and other environmental qualities, and characteristics of the exposure and experience, as well as individual parameters. We suggest that other researchers can apply this conceptual model and design data collection accordingly to target specific research questions and hypotheses (as opposed to where we have fitted already collected data). More

Zhe, W. et al. Probing the nature of soil organic matter. Crit. Rev. Environ. Sci. Technol. 52, 4072–4093 (2022).Article 

Google Scholar 
Blanco-Canqui, H. & Lal, R. Mechanisms of carbon sequestration in soil aggregates. Crit. Rev. Plant Sci. 23, 481–504 (2004).Article 
CAS 

Google Scholar 
Six, J., Paustian, K., Elliott, E. T. & Combrink, C. Soil structure and organic matter: I. Distribution of aggregate–size classes and aggregate–associated carbon. Soil Sci. Soc. Am. J. 64, 681–689 (2000).Article 
ADS 
CAS 

Google Scholar 
Lehmann, J. & Kleber, M. The contentious nature of soil organic matter. Nature 528, 60–68 (2015).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Six, J., Bossuyt, H., Degryze, S. & Denef, K. A history of research on the link between (micro)aggregates, soil biota, and soil organic matter dynamics. Soil Tillage Res. 79, 7–31 (2004).Article 

Google Scholar 
Tisdall, J. M. & Oades, J. M. Organic matter and water-stable aggregates in soils. Eur. J. Soil Sci. 33, 141–163 (1982).Article 
CAS 

Google Scholar 
Luo, Y. et al. Rice rhizodeposition promotes the build-up of organic carbon in soil via fungal necromass. Soil Biol. Biochem. 160, 108345 (2021).Article 
CAS 

Google Scholar 
Wang, X. et al. Organic amendments drive shifts in microbial community structure and keystone taxa which increase C mineralization across aggregate size classes. Soil Biol. Biochem. 153, 108062 (2021).Article 
CAS 

Google Scholar 
Duan, Y. et al. Long–term fertilisation reveals close associations between soil organic carbon composition and microbial traits at aggregate scales. Agric. Ecosyst. Environ. 306, 107169 (2021).Article 
CAS 

Google Scholar 
Christensen, B. T. Physical fractionation of soil and structural and functional complexity in organic matter turnover. Eur. J. Soil Sci. 52, 345–353 (2001).Article 
CAS 

Google Scholar 
Olk, D. C. & Gregorich, E. G. Overview of the symposium proceedings, “meaningful pools in determining soil carbon and nitrogen dynamics”. Soil Sci. Soc. Am. J. 70, 967–974 (2006).Article 
ADS 
CAS 

Google Scholar 
Courtier-Murias, D. et al. Unraveling the long–term stabilization mechanisms of organic materials in soils by physical fractionation and NMR spectroscopy. Agric. Ecosyst. Environ. 171, 9–18 (2013).Article 
CAS 

Google Scholar 
Rodrigues, L. A. T. et al. Short– and long–term effects of animal manures and mineral fertilizer on carbon stocks in subtropical soil under no–tillage. Geoderma 386, 114913 (2021).Article 
ADS 
CAS 

Google Scholar 
Mao, J., Dan, C. O., Fang, X., He, Z. & Schmidt-Rohr, K. Influence of animal manure application on the chemical structures of soil organic matter as investigated by advanced solid–state NMR and FT–IR spectroscopy. Geoderma 146, 353–362 (2008).Article 
ADS 
CAS 

Google Scholar 
Simonetti, G. et al. Characterization of humic carbon in soil aggregates in a long–term experiment with manure and mineral fertilization. Soil Sci. Soc. Am. J. 25, 880–890 (2012).Article 

Google Scholar 
Cambardella, C. A. & Elliott, E. T. Particulate soil organic-matter changes across a grassland cultivation sequence. Soil Sci. Soc. Am. J. 56, 777–783 (1992).Article 
ADS 

Google Scholar 
Conant, R. T., Six, J. & Paustian, K. Land use effects on soil carbon fractions in the southeastern United States. I. Management-intensive versus extensive grazing. Biol. Fertil. Soils 38, 386–392 (2003).Article 
CAS 

Google Scholar 
Blanco-Moure, N., Gracia, R., Bielsa, A. C. & López, M. V. Soil organic matter fractions as affected by tillage and soil texture under semiarid Mediterranean conditions. Soil Tillage Res. 155, 381–389 (2016).Article 

Google Scholar 
Yu, H. et al. Accumulation of organic C components in soil and aggregates. Sci. Rep. 5, 13804 (2015).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Schöning, I., Morgenroth, G. & Kögel-Knabner, I. O/N–alkyl and alkyl C are stabilised in fine particle size fractions of forest soils. Biogeochemistry 73, 475–497 (2005).Article 

Google Scholar 
Solomon, D., Lehmann, J., Kinyangi, J., Liang, B. & Schäfer, T. Carbon K-edge NEXAFS and FTIR–ATR spectroscopic investigation of organic carbon speciation in soils. Soil Sci. Soc. Am. J. 13, 107–119 (2005).Article 

Google Scholar 
Yan, H., Chen, C., Xu, Z., Williams, D. & Xu, J. Assessing management impacts on soil organic matter quality in subtropical Australian forests using physical and chemical fractionation as well as 13C NMR spectroscopy. Soil Biol. Biochem. 41, 640–650 (2009).Article 

Google Scholar 
Masoom, H. et al. Soil organic matter in its native state: Unravelling the most complex biomaterial on earth. Environ. Sci. Technol. 50, 1670–1680 (2016).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Vogel, C. et al. Clay mineral composition modifies decomposition and sequestration of organic carbon and nitrogen in fine soil fractions. Biol. Fertil. Soils 51, 427–442 (2015).Article 
CAS 

Google Scholar 
Sharma, S., Singh, P., Angmo, P. & Satpute, S. Total and labile pools of organic carbon in relation to soil biological properties under contrasting land-use systems in a dry mountainous region. Carbon Manage. 13, 352–371 (2022).Article 
CAS 

Google Scholar 
Six, J., Elliott, E., Paustian, K. & Doran, J. Aggregation and soil organic matter accumulation in cultivated and native grassland soils. Soil Sci. Soc. Am. J. 62, 1367–1377 (1998).Article 
ADS 
CAS 

Google Scholar 
Elliott, E. T. Aggregate structure and carbon, nitrogen, and phosphorus in native and cultivated soils. Soil Sci. Soc. Am. J. 50, 627–633 (1986).Article 
ADS 

Google Scholar 
Yu, H., Ding, W., Luo, J., Geng, R. & Cai, Z. Long-term application of organic manure and mineral fertilizers on aggregation and aggregate-associated carbon in a sandy loam soil. Soil Tillage Res. 124, 170–177 (2012).Article 

Google Scholar 
Carter, M. R. & Gregorich, E. G. (eds) Soil Sampling and Methods of Analysis 2nd edn, 230–233 (Taylor & Francis Group, CRC, 2007).
Google Scholar 
Lu, R. (ed.) Soil and Agro-chemistry Analytical Methods 146–149 (China Agricultural Science and Technology Press, 1999).
Google Scholar 
Wu, J., Joergensen, R. G., Pommerening, B., Chaussod, R. & Brookes, P. C. Measurement of soil microbial biomass C by fumigation extraction: An automated procedure. Soil Biol. Biochem. 22, 1167–1169 (1990).Article 
CAS 

Google Scholar 
Zhang, X., Zhu, A., Yang, W. & Zhang, J. Accumulation of organic components and its association with macroaggregation in a sandy loam soil following conservation tillage. Plant Soil. 416, 1–15 (2017).Article 
CAS 

Google Scholar 
Skjemstad, J. O., Clarke, P., Taylor, J. A., Oades, J. M. & Newman, R. H. The removal of magnetic materials from surface soils—a solid state 13C CP/MAS NMR study. Soil Res. 32, 1215–1229 (1994).Article 
CAS 

Google Scholar 
Ringle, C. M., Wende, S. & Becker, J. M. SmartPLS 3.” Boenningstedt: SmartPLS GmbH. Preprint at http://www.smartpls.com (2015).Jerbi, M., Labidi, S., Lounès-Hadj Sahraoui, A., Chaar, H. & Ben Jeddi, F. Higher temperatures and lower annual rainfall do not restrict, directly or indirectly, the mycorrhizal colonization of barley (Hordeum vulgare L.) under rainfed conditions. PLoS ONE 15, e0241794 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cohen, J. Statistical power analysis for the behavioral sciences 2nd edn, 407–530 (Erlbaum Associates, Berlin, 1988).MATH 

Google Scholar 
Singh, P. & Benbi, D. K. Physical and chemical stabilization of soil organic matter in cropland ecosystems under rice–wheat, maize–wheat and cotton–wheat cropping systems in northwestern India. Carbon Manag. 12, 603–621 (2021).Article 
CAS 

Google Scholar 
Kiem, R. & Kögel-Knabner, I. Contribution of lignin and polysaccharides to the refractory carbon pool in C–depleted arable soils. Soil Biol. Biochem. 35, 101–118 (2003).Article 
CAS 

Google Scholar 
Lutzow, M. V. et al. Stabilization of organic matter in temperate soils: mechanisms and their relevance under different soil conditions: a review. Eur. J. Soil Sci. 57, 426–445 (2006).Article 

Google Scholar 
Yudina, A. V., Klyueva, V. V., Romanenko, K. A. & Fomin, D. S. Micro- within macro: How micro-aggregation shapes the soil pore space and water-stability. Geoderma 415, 115771 (2022).Article 
ADS 
CAS 

Google Scholar 
Tisdall, J. M., Smith, S. E. & Rengasamy, P. Aggregation of soil by fungal hyphae. Soil Res. 35, 55–60 (1997).Article 

Google Scholar 
Li, T. et al. Contrasting impacts of manure and inorganic fertilizer applications for nine years on soil organic carbon and its labile fractions in bulk soil and soil aggregates. CATENA 194, 104739 (2020).Article 
CAS 

Google Scholar 
Liang, Y. et al. Effect of chemical fertilizer and straw-derived organic amendments on continuous maize yield, soil carbon sequestration and soil quality in a Chinese Mollisol. Agric. Ecosyst. Environ. 314, 107403 (2021).Article 
CAS 

Google Scholar 
Liang, C., Kästner, M. & Joergensen, R. G. Microbial necromass on the rise: The growing focus on its role in soil organic matter development. Soil Biol. Biochem. 150, 108000 (2020).Article 
CAS 

Google Scholar 
Sharma, S., Singh, P. & Kumar, S. Responses of soil carbon pools, enzymatic activity, and crop yields to nitrogen and straw incorporation in a rice-wheat cropping system in North-Western India. Front. Sustain. Food Syst. 4, 532704 (2020).Article 

Google Scholar 
Puget, P., Chenu, C. & Balesdent, J. Dynamics of soil organic matter associated with particle–size fractions of water–stable aggregates. Eur. J. Soil Sci. 51, 595–605 (2000).Article 

Google Scholar  More

Cohan FM. Bacterial species and speciation. Syst Biol. 2001;50:513–24.Article 
CAS 
PubMed 

Google Scholar 
Delmont TO, Kiefl E, Kilinc O, Esen OC, Uysal I, Rappe MS, et al. Single-amino acid variants reveal evolutionary processes that shape the biogeography of a global SAR11 subclade. eLife 2019;8:e46497.Article 
PubMed 
PubMed Central 

Google Scholar 
Hunt DE, David LA, Gevers D, Preheim SP, Alm EJ, Polz MF. Resource partitioning and sympatric differentiation among closely related bacterioplankton. Science 2008;320:1081–5.Article 
CAS 
PubMed 

Google Scholar 
Moore LR, Rocap G, Chisholm SW. Physiology and molecular phylogeny of coexisting Prochlorococcus ecotypes. Nature 1998;393:464–7.Article 
CAS 
PubMed 

Google Scholar 
Rivas LR. A reinterpretation of the concepts “sympatric” and “allopatric” with proposal for the additional terms “syntopic” and “allotopic”. Syst Zool. 1964;13:42–3.Article 

Google Scholar 
Friedman J, Alm EJ, Shapiro BJ. Sympatric speciation: when is it possible in bacteria? PLoS One. 2013;8:e53539.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kiene RP, Nowinski B, Esson K, Preston C, Marin R III, Birch J, et al. Unprecedented DMSP concentrations in a massive dinoflagellate bloom in Monterey Bay. Ca Geophys Res Lett. 2019;46:12279–88.Article 

Google Scholar 
Scholin CA, Birch J, Jensen S, Marin R, Massion E, Pargett D, et al. The quest to develop ecogenomic sensors a 25-year history of the environmental sample processor (ESP) as a case study. Oceanography. 2017;30:100–13.Article 

Google Scholar 
Nowinski B, Smith CB, Thomas CM, Esson K, Marin R, Preston CM, et al. Microbial metagenomes and metatranscriptomes during a coastal phytoplankton bloom. Sci Data. 2019;6:1–7.Article 
CAS 

Google Scholar 
Luo H, Löytynoja A, Moran MA. Genome content of uncultivated marine Roseobacters in the surface ocean. Environ Microbiol. 2012;14:41–51.Article 
CAS 
PubMed 

Google Scholar 
Connon SA, Giovannoni SJ. High-throughput methods for culturing microorganisms in very-low-nutrient media yield diverse new marine isolates. Appl Environ Microbiol. 2002;68:3878–85.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Feng X, Chu X, Qian Y, Henson MW, Lanclos VC, Qin F, et al. Mechanisms driving genome reduction of a novel Roseobacter lineage. ISME J. 2021;15:3576–86.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Moran MA, Belas R, Schell M, González J, Sun F, Sun S, et al. Ecological genomics of marine roseobacters. Appl Environ Microbiol. 2007;73:4559–69.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Newton RJ, Griffin LE, Bowles KM, Meile C, Gifford S, Givens CE, et al. Genome characteristics of a generalist marine bacterial lineage. ISME J. 2010;4:784–98.Article 
CAS 
PubMed 

Google Scholar 
Suzuki MT, Preston CM, Béjà O, De La Torre J, Steward G, DeLong EF. Phylogenetic screening of ribosomal RNA gene-containing clones in bacterial artificial chromosome (BAC) libraries from different depths in Monterey Bay. Micro Ecol. 2004;48:473–88.Article 
CAS 

Google Scholar 
Buchan A, González JM, Moran MA. Overview of the marine Roseobacter lineage. Appl Environ Microbiol. 2005;71:5665–77.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Giebel H-A, Kalhoefer D, Lemke A, Thole S, Gahl-Janssen R, Simon M, et al. Distribution of Roseobacter RCA and SAR11 lineages in the North Sea and characteristics of an abundant RCA isolate. ISME J. 2011;5:8–19.Article 
PubMed 

Google Scholar 
Ottesen EA, Marin R, Preston CM, Young CR, Ryan JP, Scholin CA, et al. Metatranscriptomic analysis of autonomously collected and preserved marine bacterioplankton. ISME J. 2011;5:1881–95.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang Y, Sun Y, Jiao N, Stepanauskas R, Luo H. Ecological genomics of the uncultivated marine Roseobacter lineage CHAB-I-5. Appl Environ Microbiol. 2016;82:2100–11.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Aylward FO, Eppley JM, Smith JM, Chavez FP, Scholin CA, DeLong EF. Microbial community transcriptional networks are conserved in three domains at ocean basin scales. Proc Nat Acad Sci. 2015;112:5443–8.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ottesen EA, Young CR, Eppley JM, Ryan JP, Chavez FP, Scholin CA, et al. Pattern and synchrony of gene expression among sympatric marine microbial populations. Proc Nat Acad Sci. 2013;110:E488–E97.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Nowinski B, Motard‐Côté J, Landa M, Preston CM, Scholin CA, Birch JM, et al. Microdiversity and temporal dynamics of marine bacterial dimethylsulfoniopropionate genes. Environ Microbiol. 2019;21:1687–701.Article 
CAS 
PubMed 

Google Scholar 
Mukherjee S, Stamatis D, Bertsch J, Ovchinnikova G, Sundaramurthi JC, Lee J, et al. Genomes OnLine Database (GOLD) v. 8: overview and updates. Nucleic Acids Res. 2021;49:D723–D33.Article 
CAS 
PubMed 

Google Scholar 
Chen I-MA, Chu K, Palaniappan K, Pillay M, Ratner A, Huang J, et al. IMG/M v. 5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res. 2019;47:D666–D77.Article 
CAS 
PubMed 

Google Scholar 
Satinsky BM, Gifford SM, Crump BC, Moran MA. Use of internal standards for quantitative metatranscriptome and metagenome analysis. In: DeLong EF, editor. Methods in Enzymology 531: Elsevier; 2013. p. 237–50.Satinsky BM, Gifford SM, Crump BC, Smith C.Moran MA, Internal genomic DNA standard for quantitative metagenome analysis V3. protocols io 2017; https://doi.org/10.17504/protocols.io.jxdcpi6p.Satinsky BM, Gifford SM, Crump BC, Smith C.Moran MA, Preparation of custom synthesized RNAtranscript standard V3. protocols io. 2017; https://doi.org/10.17504/protocols.io.jxccpiwp.Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 2015;3:e1165.Article 
PubMed 
PubMed Central 

Google Scholar 
Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rodriguez-R LM, Konstantinidis KT. The enveomics collection: a toolbox for specialized analyses of microbial genomes and metagenomes. PeerJ Prepr. 2016. Report No.: 2167–9843Lee K, Choo Y-J, Giovannoni SJ, Cho J-C. Maritimibacter alkaliphilus gen. nov., sp. nov., a genome-sequenced marine bacterium of the Roseobacter clade in the order Rhodobacterales. Int J Syst Evol Microbiol. 2007;57:1653–8.Article 
PubMed 

Google Scholar 
Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ 2015;3:e1319.Article 
PubMed 
PubMed Central 

Google Scholar 
Tatusov RL, Galperin MY, Natale DA, Koonin EV. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 2000;28:33–6.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, Von Mering C, et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol Biol Evol. 2017;34:2115–22.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Aramaki T, Blanc-Mathieu R, Endo H, Ohkubo K, Kanehisa M, Goto S, et al. KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 2020;36:2251–2.Article 
CAS 
PubMed 

Google Scholar 
Bushnell B. BBMap: a fast, accurate, splice-aware aligner. No. LBNL-7065E. Lawrence Berkeley National Laboratory, Berkeley, CA (United States); 2014.Markowitz VM, Chen I-MA, Palaniappan K, Chu K, Szeto E, Grechkin Y, et al. The integrated microbial genomes system: an expanding comparative analysis resource. Nucleic Acids Res. 2010;38:D382–D90.Article 
CAS 
PubMed 

Google Scholar 
Sun Y, Luo H. Homologous recombination in core genomes facilitates marine bacterial adaptation. Appl Environ Microbiol. 2018;84:e02545–17.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pfaffel O. ClustImpute: An R package for K-means clustering with build-in missing data imputation. https://www.researchgate.net/publication/341881683.Moran MA, Satinsky B, Gifford SM, Luo H, Rivers A, Chan L-K, et al. Sizing up metatranscriptomics. ISME J 2013;7:237–43.Article 
CAS 
PubMed 

Google Scholar 
Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, et al. Structure and function of the global ocean microbiome. Science. 2015;348:1261359.Article 
PubMed 

Google Scholar 
Gifford SM, Zhao L, Stemple B, DeLong K, Medeiros PM, Seim H, et al. Microbial niche diversification in the Galápagos Archipelago and its response to El Niño. Front Microbiol. 2020;11:575194.Article 
PubMed 
PubMed Central 

Google Scholar 
Rich VI, Pham VD, Eppley J, Shi Y, DeLong EF. Time‐series analyses of Monterey Bay coastal microbial picoplankton using a ‘genome proxy’microarray. Environ Microbiol. 2011;13:116–34.Article 
CAS 
PubMed 

Google Scholar 
Riedel T, Tomasch J, Buchholz I, Jacobs J, Kollenberg M, Gerdts G, et al. Constitutive expression of the proteorhodopsin gene by a flavobacterium strain representative of the proteorhodopsin-producing microbial community in the North Sea. Appl Environ Microbiol. 2010;76:3187–97.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Iverson V, Morris RM, Frazar CD, Berthiaume CT, Morales RL, Armbrust EV. Untangling genomes from metagenomes: revealing an uncultured class of marine Euryarchaeota. Science 2012;335:587–90.Article 
CAS 
PubMed 

Google Scholar 
Yooseph S, Nealson KH, Rusch DB, McCrow JP, Dupont CL, Kim M, et al. Genomic and functional adaptation in surface ocean planktonic prokaryotes. Nature 2010;468:60–6.Article 
CAS 
PubMed 

Google Scholar 
Wagner-Döbler I, Biebl H. Environmental biology of the marine Roseobacter lineage. Annu Rev Microbiol. 2006;60:255–80.Article 
PubMed 

Google Scholar 
West NJ, Obernosterer I, Zemb O, Lebaron P. Major differences of bacterial diversity and activity inside and outside of a natural iron‐fertilized phytoplankton bloom in the Southern Ocean. Environ Microbiol. 2008;10:738–56.Article 
CAS 
PubMed 

Google Scholar 
Luo H, Moran MA. Evolutionary ecology of the marine Roseobacter clade. Microbiol Mol Biol Rev. 2014;78:573–87.Article 
PubMed 
PubMed Central 

Google Scholar 
Simon M, Scheuner C, Meier-Kolthoff JP, Brinkhoff T, Wagner-Döbler I, Ulbrich M, et al. Phylogenomics of Rhodobacteraceae reveals evolutionary adaptation to marine and non-marine habitats. ISME J 2017;11:1483–99.Article 
PubMed 
PubMed Central 

Google Scholar 
Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Comm. 2018;9:1–8.Article 

Google Scholar 
Caro‐Quintero A, Konstantinidis KT. Bacterial species may exist, metagenomics reveal. Environ Microbiol. 2012;14:347–55.Article 
PubMed 

Google Scholar 
Tindall BJ, Rosselló-Móra R, Busse H-J, Ludwig W, Kämpfer P. Notes on the characterization of prokaryote strains for taxonomic purposes. Int J Syst Evol Microbiol. 2010;60:249–66.Article 
CAS 
PubMed 

Google Scholar 
Cohan FM. What are bacterial species? Ann Rev Microbiol. 2002;56:457–87.Article 
CAS 

Google Scholar 
Mende DR, Sunagawa S, Zeller G, Bork P. Accurate and universal delineation of prokaryotic species. Nat Meth. 2013;10:881–4.Article 
CAS 

Google Scholar 
Olm MR, Crits-Christoph A, Diamond S, Lavy A, Matheus Carnevali PB, Banfield JF. Consistent metagenome-derived metrics verify and delineate bacterial species boundaries. mSystems 2020;5:e00731–19.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Konstantinidis KT, Tiedje JM. Prokaryotic taxonomy and phylogeny in the genomic era: advancements and challenges ahead. Curr Opin Microbiol. 2007;10:504–9.Article 
CAS 
PubMed 

Google Scholar 
Delmont TO, Eren EM. Linking pangenomes and metagenomes: The Prochlorococcus metapangenome. PeerJ 2018;2018:e4320–e.Article 

Google Scholar 
Neidhardt F, Umbarger H Chemical composition of Escherichia coli. In: FC N, Curtiss R III, JL I, ECC L, KB L, B M, et al., editors. Escherichia coli and Salmonella typhimurium: Cellular and Molecular Biology. Washington DC: ASM Press; 1996. p. 13-6.Taniguchi Y, Choi PJ, Li G-W, Chen H, Babu M, Hearn J, et al. Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science. 2010;329:533–8.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rodríguez-Gijón A, Nuy JK, Mehrshad M, Buck M, Schulz F, Woyke T, et al. A genomic perspective across Earth’s microbiomes reveals that genome size in Archaea and Bacteria is linked to ecosystem type and trophic strategy. Front Microbiol. 2022;12:761869.Article 
PubMed 
PubMed Central 

Google Scholar 
Ryu K-S, Kim C, Kim I, Yoo S, Choi B-S, Park C. NMR application probes a novel and ubiquitous family of enzymes that alter monosaccharide configuration. J Biol Chem. 2004;279:25544–8.Article 
CAS 
PubMed 

Google Scholar 
Giachino A, Waldron KJ. Copper tolerance in bacteria requires the activation of multiple accessory pathways. Mol Microbiol. 2020;114:377–90.Article 
CAS 
PubMed 

Google Scholar 
Wang X, Zhang Y, Ren M, Xia T, Chu X, Liu C, et al. Cryptic speciation of a pelagic Roseobacter population varying at a few thousand nucleotide sites. ISME J. 2020;14:3106–19.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Uchimiya M, Schroer W, Olofsson M, Edison AS, Moran MA. Diel investments in metabolite production and consumption in a model microbial system. ISME J. 2022;16:1306–17.Article 
CAS 
PubMed 

Google Scholar 
Cordero OX, Wildschutte H, Kirkup B, Proehl S, Ngo L, Hussain F, et al. Ecological populations of bacteria act as socially cohesive units of antibiotic production and resistance. Science 2012;337:1228–31.Article 
CAS 
PubMed 

Google Scholar 
Morris JJ, Lenski RE, Zinser ER. The Black Queen Hypothesis: evolution of dependencies through adaptive gene loss. mBio 2012;3:e00036–12.Article 
PubMed 
PubMed Central 

Google Scholar 
Environmental data from CTD during the Fall 2016 ESP deployment in Monterey Bay, CA. Biological and Chemical Oceanography Data Management Office (BCO-DMO). 2019. Available from: https://doi.org/10.1575/1912/bco-dmo.756376.1.Environmental data from Niskin bottle sampling during the Fall 2016 ESP deployment in Monterey Bay. Biological and Chemical Oceanography Data Management Office (BCO-DMO). 2019. Available from: https://doi.org/10.1575/1912/bco-dmo.756413.1. More

Jayathilake PG, Jana S, Rushton S, Swailes D, Bridgens B, Curtis T, et al. Extracellular polymeric substance production and aggregated bacteria colonization influence the competition of microbes in biofilms. Front Microbiol. 2017;8:1865.Article 
PubMed 
PubMed Central 

Google Scholar 
Flemming H-C, Neu TR, Wingender J (eds). The Perfect Slime: Microbial Extracellular Polymeric Substances (EPS). IWA Publishing, London, 2016.Morales-García AL, Bailey RG, Jana S, Burgess JG. The role of polymers in cross-kingdom bioadhesion. Philos Trans R Soc Lond B Biol Sci. 2019;374:20190192.Article 
PubMed 
PubMed Central 

Google Scholar 
Davey ME, O’Toole GA. Microbial biofilms: From ecology to molecular genetics. Microbiol Mol. 2000;64:847–67.Article 
CAS 

Google Scholar 
Peters BM, Jabra-Rizk MA, O’May GA, Costerton JW, Shirtliff ME. Polymicrobial interactions: Impact on pathogenesis and human disease. Clin Microbiol Rev. 2012;25:193–213.Article 
PubMed 
PubMed Central 

Google Scholar 
Karygianni L, Ren Z, Koo H, Thurnheer T. Biofilm matrixome: Extracellular components in structured microbial communities. Trends Microbiol. 2020;28:668–81.Article 
CAS 
PubMed 

Google Scholar 
Morris BEL, Henneberger R, Huber H, Moissl-Eichinger C. Microbial syntrophy: Interaction for the common good. FEMS Microbiol Rev. 2013;37:384–406.Article 
CAS 
PubMed 

Google Scholar 
Decho AW, Gutierrez T. Microbial extracellular polymeric substances (EPSs) in ocean systems. Front Microbiol. 2017;8:922.Article 
PubMed 
PubMed Central 

Google Scholar 
Boleij M, Seviour T, Wong LL, van Loosdrecht MCM, Lin Y. Solubilization and characterization of extracellular proteins from anammox granular sludge. Water Res. 2019;164:114952.Article 
CAS 
PubMed 

Google Scholar 
Kim D, Barraza JP, Arthur RA, Hara A, Lewis K, Liu Y, et al. Spatial mapping of polymicrobial communities reveals a precise biogeography associated with human dental caries. Proc Natl Acad Sci USA. 2020;117:12375–86.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sadiq FA, Burmølle M, Heyndrickx M, Flint S, Lu W, Chen W, et al. Community-wide changes reflecting bacterial interspecific interactions in multispecies biofilms. Crit Rev Microbiol. 2021;47:338–58.Article 
PubMed 

Google Scholar 
Liu W, Jacquiod S, Brejnrod A, Russel J, Burmølle M, Sørensen SJ. Deciphering links between bacterial interactions and spatial organization in multispecies biofilms. ISME J. 2019;13:3054–66.Article 
PubMed 
PubMed Central 

Google Scholar 
Bridier A, Le Coq D, Dubois-Brissonnet F, Thomas V, Aymerich S, Briandet R. The spatial architecture of Bacillus subtilis biofilms deciphered using a surface-associated model and in situ imaging. PLOS One. 2011;6:e16177.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lee KWK, Periasamy S, Mukherjee M, Xie C, Kjelleberg S, Rice SA. Biofilm development and enhanced stress resistance of a model, mixed-species community biofilm. ISME J. 2014;8:894–907.Article 
CAS 
PubMed 

Google Scholar 
Myszka K, Czaczyk K. Characterization of adhesive exopolysaccharide (EPS) produced by Pseudomonas aeruginosa under starvation conditions. Curr Microbiol. 2009;58:541–6.Article 
CAS 
PubMed 

Google Scholar 
Harimawan A, Ting YP. Investigation of extracellular polymeric substances (EPS) properties of P. aeruginosa and B. subtilis and their role in bacterial adhesion. Colloids Surf B. 2016;146:459–67.Article 
CAS 

Google Scholar 
Yang X-R, Li H, Nie S-A, Su J-Q, Weng B-S, Zhu G-B, et al. Potential contribution of anammox to nitrogen loss from paddy soils in Southern China. Appl Environ Microbiol. 2015;81:938–47.Article 
PubMed 
PubMed Central 

Google Scholar 
Kartal B, van Niftrik L, Keltjens JT, Op den Camp HJM, Jetten MSM Chapter 3 – Anammox—Growth physiology, cell biology, and metabolism. In: Poole RK, editor. Adv Microb Physiol. 60: Academic Press; 2012. p. 211–62.Lu Y, Natarajan G, Nguyen TQN, Thi SS, Arumugam K, Seviour TW, et al. Species level enrichment of AnAOB and associated growth morphology under the effect of key metabolites. bioRxiv. 2020. 2020.02.04.934877Gonzalez-Gil G, Sougrat R, Behzad AR, Lens PN, Saikaly PE. Microbial community composition and ultrastructure of granules from a full-scale anammox reactor. Micro Ecol. 2015;70:118–31.Article 
CAS 

Google Scholar 
Kindaichi T, Yuri S, Ozaki N, Ohashi A. Ecophysiological role and function of uncultured Chloroflexi in an anammox reactor. Water Sci Technol. 2012;66:2556–61.Article 
CAS 
PubMed 

Google Scholar 
Qin Y, Han B, Cao Y, Wang T. Impact of substrate concentration on anammox-UBF reactors start-up. Bioresour Technol. 2017;239:422–9.Article 
CAS 
PubMed 

Google Scholar 
Chen Z, Meng Y, Sheng B, Zhou Z, Jin C, Meng F. Linking exoproteome function and structure to anammox biofilm development. Environ Sci Technol. 2019;53:1490–500.Article 
CAS 
PubMed 

Google Scholar 
Ali M, Shaw DR, Albertsen M, Saikaly PE. Comparative genome-centric analysis of freshwater and marine ANAMMOX cultures suggests functional redundancy in nitrogen removal processes. Front Microbiol. 2020;11:1637.Article 
PubMed 
PubMed Central 

Google Scholar 
Jia F, Yang Q, Liu X, Li X, Li B, Zhang L, et al. Stratification of extracellular polymeric substances (EPS) for aggregated anammox microorganisms. Environ Sci Technol. 2017;51:3260–8.Article 
CAS 
PubMed 

Google Scholar 
Hou X, Liu S, Zhang Z. Role of extracellular polymeric substance in determining the high aggregation ability of anammox sludge. Water Res. 2015;75:51–62.Article 
CAS 
PubMed 

Google Scholar 
Feng C, Lotti T, Lin Y, Malpei F. Extracellular polymeric substances extraction and recovery from anammox granules: Evaluation of methods and protocol development. Chem Eng J. 2019;374:112–22.Article 
CAS 

Google Scholar 
Lotti T, Carretti E, Berti D, Montis C, Del Buffa S, Lubello C, et al. Hydrogels formed by anammox extracellular polymeric substances: Structural and mechanical insights. Sci Rep. 2019;9:11633.Article 
PubMed 
PubMed Central 

Google Scholar 
Jakubovics NS, Goodman SD, Mashburn-Warren L, Stafford GP, Cieplik F. The dental plaque biofilm matrix. Periodontology 2000. 2021;86:32–56.Article 
PubMed 
PubMed Central 

Google Scholar 
Ðapa T, Leuzzi R, Ng YK, Baban ST, Adamo R, Kuehne SA, et al. Multiple factors modulate biofilm formation by the anaerobic pathogen Clostridium difficile. J Bacteriol. 2013;195:545–55.Article 
PubMed 
PubMed Central 

Google Scholar 
Honma K, Inagaki S, Okuda K, Kuramitsu HK, Sharma A. Role of a Tannerella forsythia exopolysaccharide synthesis operon in biofilm development. Micro Pathog. 2007;42:156–66.Article 
CAS 

Google Scholar 
Li X-R, Du B, Fu H-X, Wang R-F, Shi J-H, Wang Y, et al. The bacterial diversity in an anaerobic ammonium-oxidizing (anammox) reactor community. Syst Appl Microbiol. 2009;32:278–89.Article 
CAS 
PubMed 

Google Scholar 
Cho S, Takahashi Y, Fujii N, Yamada Y, Satoh H, Okabe S. Nitrogen removal performance and microbial community analysis of an anaerobic up-flow granular bed anammox reactor. Chemosphere 2010;78:1129–35.Article 
CAS 
PubMed 

Google Scholar 
Morgenroth E, Sherden T, Van Loosdrecht MCM, Heijnen JJ, Wilderer PA. Aerobic granular sludge in a sequencing batch reactor. Water Res. 1997;31:3191–4.Article 
CAS 

Google Scholar 
Wong LL, Natarajan G, Boleij M, Thi SS, Winnerdy FR, Mugunthan S, et al. Extracellular protein isolation from the matrix of anammox biofilm using ionic liquid extraction. Appl Microbiol Biotechnol. 2020;104:3643–54.Article 
CAS 
PubMed 

Google Scholar 
Law Y, Kirkegaard RH, Cokro AA, Liu X, Arumugam K, Xie C, et al. Integrative microbial community analysis reveals full-scale enhanced biological phosphorus removal under tropical conditions. Sci Rep. 2016;6:25719.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ondov BD, Bergman NH, Phillippy AM. Interactive metagenomic visualization in a Web browser. BMC Bioinform. 2011;12:385.Article 

Google Scholar 
Liu X, Arumugam K, Natarajan G, Seviour TW, Drautz-Moses DI, Wuertz S, et al. Draft genome sequence of a Candidatus brocadia bacterium enriched from activated sludge collected in a tropical climate. Genome Announc. 2018;6:e00406–18.Article 
PubMed 
PubMed Central 

Google Scholar 
Price MN, Dehal PS, Arkin AP. FastTree 2-approximately maximum-likelihood trees for large alignments. PloS One. 2010;5:e9490–e.Article 
PubMed 
PubMed Central 

Google Scholar 
Arkin AP, Cottingham RW, Henry CS, Harris NL, Stevens RL, Maslov S, et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nat Biotechnol. 2018;36:566–9.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Buchfink B, Reuter K, Drost H-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat Methods. 2021;18:366–8.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.Article 
CAS 
PubMed 

Google Scholar 
Daims H, Nielsen JL, Nielsen PH, Schleifer K-H, Wagner M. In situ characterization of Nitrospira-like nitrite-oxidizing bacteria active in wastewater treatment plants. Appl Environ Microbiol. 2001;67:5273–84.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Seviour T, Wong LL, Lu Y, Mugunthan S, Yang Q, Shankari UDOCS, et al. Phase transitions by an abundant protein in the anammox extracellular matrix mediate cell-to-cell aggregation and biofilm formation. mBio 2020;11:e02052–20.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Protter DSW, Rao BS, Van Treeck B, Lin Y, Mizoue L, Rosen MK, et al. Intrinsically disordered regions can contribute promiscuous interactions to RNP granule assembly. Cell Rep. 2018;22:1401–12.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Fulton KM, Smith JC, Twine SM. Clinical applications of bacterial glycoproteins. Expert Rev Proteom. 2016;13:345–53.Article 
CAS 

Google Scholar 
Upreti RK, Kumar M, Shankar V. Bacterial glycoproteins: Functions, biosynthesis and applications. Proteomics 2003;3:363–79.Article 
CAS 
PubMed 

Google Scholar 
van Teeseling MCF, Maresch D, Rath CB, Figl R, Altmann F, Jetten MSM, et al. The S-layer protein of the anammox bacterium Kuenenia stuttgartiensiss is heavily O-glycosylated. Front Microbiol. 2016;7:1721.PubMed 
PubMed Central 

Google Scholar 
McGonigle JM, Lang SQ, Brazelton WJ, Parales RE. Genomic evidence for formate metabolism by Chloroflexi as the key to unlocking deep carbon in lost city microbial ecosystems. Appl Environ Microbiol. 2020;86:e02583–19.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Vuillemin A, Kerrigan Z, D’Hondt S, Orsi WD. Exploring the abundance, metabolic potential and gene expression of subseafloor Chloroflexi in million-year-old oxic and anoxic abyssal clay. FEMS Microbiol Ecol. 2020;96:fiaa223.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kartal B, de Almeida NM, Maalcke WJ, Op den Camp HJ, Jetten MS, Keltjens JT. How to make a living from anaerobic ammonium oxidation. FEMS Microbiol Rev. 2013;37:428–61.Article 
CAS 
PubMed 

Google Scholar 
Loera-Muro A, Guerrero-Barrera A, Tremblay DNY, Hathroubi S, Angulo C. Bacterial biofilm-derived antigens: A new strategy for vaccine development against infectious diseases. Expert Rev Vaccines. 2021;20:385–96.Article 
CAS 
PubMed 

Google Scholar 
Hobley L, Harkins C, MacPhee CE, Stanley-Wall NR. Giving structure to the biofilm matrix: An overview of individual strategies and emerging common themes. FEMS Microbiol Rev. 2015;39:649–69.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Elias S, Banin E. Multi-species biofilms: Living with friendly neighbors. FEMS Microbiol Rev. 2012;36:990–1004.Article 
CAS 
PubMed 

Google Scholar 
Teeseling MCFV, Almeida NMD, Klingl A, Speth DR, Camp HJMOD, Rachel R, et al. A new addition to the cell plan of anammox bacteria: Candidatus Kuenenia stuttgartiensis has a protein surface layer as the outermost layer of the cell. J Bacteriol. 2014;196:80–9.Article 
PubMed 
PubMed Central 

Google Scholar 
Paula AJ, Hwang G, Koo H. Dynamics of bacterial population growth in biofilms resemble spatial and structural aspects of urbanization. Nat Commun. 2020;11:1354.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kragelund C, Caterina L, Borger A, Thelen K, Eikelboom D, Tandoi V, et al. Identity, abundance and ecophysiology of filamentous Chloroflexi species present in activated sludge treatment plants. FEMS Microbiol Ecol. 2007;59:671–82.Article 
CAS 
PubMed 

Google Scholar 
Nierychlo M, Miłobędzka A, Petriglieri F, McIlroy B, Nielsen PH, McIlroy SJ. The morphology and metabolic potential of the Chloroflexi in full-scale activated sludge wastewater treatment plants. FEMS Microbiol Ecol. 2018;95.Kragelund C, Thomsen TR, Mielczarek AT, Nielsen PH. Eikelboom’s morphotype 0803 in activated sludge belongs to the genus Caldilinea in the phylum Chloroflexi. FEMS Microbiol Ecol. 2011;76:451–62.Article 
CAS 
PubMed 

Google Scholar 
Zhang J, Miao Y, Zhang Q, Sun Y, Wu L, Peng Y. Mechanism of stable sewage nitrogen removal in a partial nitrification-anammox biofilm system at low temperatures: Microbial community and EPS analysis. Bioresour Technol. 2020;297:122459.Article 
CAS 
PubMed 

Google Scholar 
Björnsson L, Hugenholtz P, Tyson GW, Blackall LL. Filamentous Chloroflexi (green non-sulfur bacteria) are abundant in wastewater treatment processes with biological nutrient removal. Microbiology 2002;148:2309–18.Article 
PubMed 

Google Scholar 
Boleij M, Pabst M, Neu TR, van Loosdrecht MCM, Lin Y. Identification of glycoproteins isolated from extracellular polymeric substances of full-scale anammox granular sludge. Environ Sci Technol. 2018;52:13127–35.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pabst M, Grouzdev DS, Lawson CE, Kleikamp HBC, de Ram C, Louwen R, et al. A general approach to explore prokaryotic protein glycosylation reveals the unique surface layer modulation of an anammox bacterium. ISME J. 2022;16:346–57.Article 
CAS 
PubMed 

Google Scholar 
Berlanga M, Guerrero R. Living together in biofilms: The microbial cell factory and its biotechnological implications. Micro Cell Fact. 2016;15:165.Article 

Google Scholar 
Liu T, Tian R, Li Q, Wu N, Quan X. Strengthened attachment of anammox bacteria on iron-based modified carrier and its effects on anammox performance in integrated floating-film activated sludge (IFFAS) process. Sci Total Environ. 2021;787:147679.Article 
CAS 
PubMed 

Google Scholar  More

Teale, S. A. & Castello, J. D. The past as key to the future: a new perspective on forest health. In Forest Health: An Integrated Perspective (eds Castello, J. D. & Teale, S. A.) 3–16 (Cambridge University Press, 2011). https://doi.org/10.1017/CBO9780511974977.002.Chapter 

Google Scholar 
Jactel, H., Koricheva, J. & Castagneyrol, B. Responses of forest insect pests to climate change: Not so simple. Curr. Opin. Insect Sci. 35, 103–108 (2019).Article 
PubMed 

Google Scholar 
Trumbore, S., Brando, P. & Hartmann, H. Forest health and global change. Science 349, 814–818 (2015).Article 
ADS 
CAS 
PubMed 

Google Scholar 
North, M. P. et al. Operational resilience in western US frequent-fire forests. For. Ecol. Manag. 507, 120004 (2022).Article 

Google Scholar 
Raffa, K. F. et al. A literal use of “forest health” safeguards against misuse and misapplication. J. For. 107, 276–277 (2009).
Google Scholar 
Kolb, T. E., Wagner, M. R. & Covington, W. W. Concepts of forest health: Utilitarian and ecosystem perspectives. J. For. 92, 10–15 (1994).
Google Scholar 
Cale, J. A. et al. A quantitative index of forest structural sustainability. Forests 5, 1618–1634 (2014).Article 

Google Scholar 
Lintz, H. E. et al. Quantifying density-independent mortality of temperate tree species. Ecol. Indic. 66, 1–9 (2016).Article 

Google Scholar 
Stanke, H., Finley, A. O., Domke, G. M., Weed, A. S. & MacFarlane, D. W. Over half of western United States’ most abundant tree species in decline. Nat. Commun. 12, 451 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bettinger, P., Boston, K., Siry, J. P. & Grebner, D. L. Chapter 2—Valuing and Characterizing Forest Conditions. In Forest Management and Planning (eds Bettinger, P. et al.) 21–63 (Academic Press, 2017). https://doi.org/10.1016/B978-0-12-809476-1.00002-3.Chapter 

Google Scholar 
Crowther, T. W. et al. Mapping tree density at a global scale. Nature 525, 201–205 (2015).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Fettig, C. J. et al. The effectiveness of vegetation management practices for prevention and control of bark beetle infestations in coniferous forests of the western and southern United States. For. Ecol. Manag. 238, 24–53 (2007).Article 

Google Scholar 
Morin, R. S. & Liebhold, A. M. Invasions by two non-native insects alter regional forest species composition and successional trajectories. For. Ecol. Manag. 341, 67–74 (2015).Article 

Google Scholar 
Nowak, J. T., Meeker, J. R., Coyle, D. R., Steiner, C. A. & Brownie, C. Southern pine beetle infestations in relation to forest stand conditions, previous thinning, and prescribed burning: Evaluation of the southern pine beetle prevention program. J. For. 113, 454–462 (2015).
Google Scholar 
Asaro, C. & Chamberlin, L. A. Outbreak history (1953–2014) of spring defoliators impacting oak-dominated forests in Virginia, with emphasis on gypsy moth (Lymantria dispar L.) and fall cankerworm (Alsophila pometaria Harris). Am. Entomol. 61, 174–185 (2015).Article 

Google Scholar 
Negrón, J. F. Probability of infestation and extent of mortality associated with the Douglas-fir beetle in the Colorado Front Range. For. Ecol. Manag. 107, 71–85 (1998).Article 

Google Scholar 
Negrón, J. F. & Popp, J. B. Probability of ponderosa pine infestation by mountain pine beetle in the Colorado Front Range. For. Ecol. Manag. 191, 17–27 (2004).Article 

Google Scholar 
Schmid, J. M. & Frye, R. H. Spruce Beetle in the Rockies. Gen. Tech. Rep. RM-49 (US Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station, 1977).
Google Scholar 
Krivak-Tetley, F. E. et al. Aggressive tree killer or natural thinning agent? Assessing the impacts of a globally important forest insect. For. Ecol. Manag. 483, 118728 (2021).Article 

Google Scholar 
Bradford, J. B. et al. Tree mortality response to drought-density interactions suggests opportunities to enhance drought resistance. J. Appl. Ecol. 59, 549–559 (2022).Article 

Google Scholar 
Young, D. J. N. et al. Long-term climate and competition explain forest mortality patterns under extreme drought. Ecol. Lett. 20, 78–86 (2017).Article 
PubMed 

Google Scholar 
Furniss, T. J., Das, A. J., van Mantgem, P. J., Stephenson, N. L. & Lutz, J. A. Crowding, climate, and the case for social distancing among trees. Ecol. Appl. 32, e2507 (2022).Article 
PubMed 

Google Scholar 
Woodall, C. W. & Weiskittel, A. R. Relative density of United States forests has shifted to higher levels over last two decades with important implications for future dynamics. Sci. Rep. 11, 1–12 (2021).Article 

Google Scholar 
Gandhi, K. J. K., Campbell, F. & Abrams, J. Current status of forest health policy in the United States. Insects 10, 1–14 (2019).Article 

Google Scholar 
Ciesla, W. M. The role of human activities on forest insect outbreaks worldwide. Int. For. Rev. 17, 269–281 (2015).
Google Scholar 
Jactel, H. & Brockerhoff, E. G. Tree diversity reduces herbivory by forest insects. Ecol. Lett. 10, 835–848 (2007).Article 
PubMed 

Google Scholar 
Marini, L., Ayres, M. P. & Jactel, H. Impact of stand and landscape management on forest pest damage. Annu. Rev. Entomol. 67, 181–199 (2022).Article 
PubMed 

Google Scholar 
Guyot, V., Castagneyrol, B., Vialatte, A., Deconchat, M. & Jactel, H. Tree diversity reduces pest damage in mature forests across Europe. Biol. Lett. 12, 20151037 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Kneeshaw, D. D. et al. The vision of managing for pest-resistant landscapes: Realistic or utopic? Curr. For. Rep. 7, 97–113 (2021).PubMed 
PubMed Central 

Google Scholar 
Chisholm, P. J., Stevens-Rumann, C. S. & Davis, T. S. Interactions between climate and stand conditions predict pine mortality during a bark beetle outbreak. Forests 12, 360 (2021).Article 

Google Scholar 
Ferrell, G. T., Otrosina, W. J. & Demars, C. J. Predicting susceptibility of white fir during a drought-associated outbreak of the fir engraver, Scolytus ventralis in California. Can. J. For. Res. 24, 302–305 (1994).Article 

Google Scholar 
Asaro, C., Nowak, J. T. & Elledge, A. Why have southern pine beetle outbreaks declined in the southeastern U.S. with the expansion of intensive pine silviculture? A brief review of hypotheses. For. Ecol. Manag. 391, 338–348 (2017).Article 

Google Scholar 
Nowak, J. T., Klepzig, K. D., Coyle, D. R., Carothers, W. A. & Gandhi, K. J. K. Southern pine beetles in central hardwood forests: Frequency, spatial extent, and changes to forest structure. In Managing Forest Ecosystems Volume 32: Natural Disturbances and Historic Range of Variation (eds Greenberg, C. H. & Collins, B. S.) 73–88 (Springer International Publishing, 2016). https://doi.org/10.1007/978-3-319-21527-3_4.Chapter 

Google Scholar 
Crocker, S. J., Liknes, G. C., McKee, F. R., Albers, J. S. & Aukema, B. H. Stand-level factors associated with resurging mortality from eastern larch beetle (Dendroctonus simplex LeConte). For. Ecol. Manag. 375, 27–34 (2016).Article 

Google Scholar 
Mattson, W. J. & Addy, N. D. Phytophagous insects as regulators of forest primary production. Science 190, 515–522 (1975).Article 
ADS 

Google Scholar 
Thom, D. & Seidl, R. Natural disturbance impacts on ecosystem services and biodiversity in temperate and boreal forests. Biol. Rev. 91, 760–781 (2016).Article 
PubMed 

Google Scholar 
Grégoire, J. C., Raffa, K. F. & Lindgren, B. S. Economics and politics of bark beetles. In Bark Beetles: Biology and Ecology of Native and Invasive Species (eds Vega, F. E. & Hofstetter, R. W.) 585–613 (Academic Press, 2015). https://doi.org/10.1016/B978-0-12-417156-5.00015-0.Chapter 

Google Scholar 
Kolb, T. E. et al. Observed and anticipated impacts of drought on forest insects and diseases in the United States. For. Ecol. Manag. 380, 321–334 (2016).Article 

Google Scholar 
Fettig, C. J. et al. Changing climates, changing forests: A western North American perspective. J. For. 111, 214–228 (2013).
Google Scholar 
Liebhold, A. M. et al. A highly aggregated geographical distribution of forest pest invasions in the USA. Divers. Distrib. 19, 1208–1216 (2013).Article 

Google Scholar 
Siegert, N. W., Mccullough, D. G., Liebhold, A. M. & Telewski, F. W. Dendrochronological reconstruction of the epicentre and early spread of emerald ash borer in North America. Divers. Distrib. 20, 847–858 (2014).Article 

Google Scholar 
Smith, A., Herms, D. A., Long, R. P. & Gandhi, K. J. K. Community composition and structure had no effect on forest susceptibility to invasion by the emerald ash borer (Coleoptera: Buprestidae). Can. Entomol. 147, 318–328 (2015).Article 

Google Scholar 
Aukema, J. E. et al. Historical accumulation of nonindigenous forest pests in the continental United States. Bioscience 60, 886–897 (2010).Article 

Google Scholar 
Hicke, J. A. et al. Effects of biotic disturbances on forest carbon cycling in the United States and Canada. Glob. Chang. Biol. 18, 7–34 (2012).Article 
ADS 

Google Scholar 
Feeny, P. Seasonal changes in oak leaf tannins and nutrients as a cause of spring feeding by winter moth caterpillars. Ecology 51, 565–581 (1970).Article 

Google Scholar 
Schowalter, T. D., Hargrove, W. W. & Crossley, D. A. Herbivory in forested ecosystems. Annu. Rev. Entomol. 31, 177–196 (1986).Article 

Google Scholar 
Seidl, R. et al. Forest disturbances under climate change. Nat. Clim. Chang. 7, 395–402 (2017).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Colautti, R. I., Ricciardi, A., Grigorovich, I. A. & MacIsaac, H. J. Is invasion success explained by the enemy release hypothesis? Ecol. Lett. 7, 721–733 (2004).Article 

Google Scholar 
Catford, J. A., Jansson, R. & Nilsson, C. Reducing redundancy in invasion ecology by integrating hypotheses into a single theoretical framework. Divers. Distrib. 15, 22–40 (2009).Article 

Google Scholar 
Guyot, V. et al. Tree diversity limits the impact of an invasive forest pest. PLoS One 10, 1–16 (2015).Article 

Google Scholar 
Root, R. B. Organization of a plant-arthropod association in simple and diverse habitats: The fauna of collards (Brassica oleracea). Ecol. Monogr. 43, 95–124 (1973).Article 

Google Scholar 
Acker, S. A., Boetsch, J. R., Fallon, B. & Denn, M. Stable background tree mortality in mature and old-growth forests in western Washington (NW USA). For. Ecol. Manag. 532, 120817 (2023).Article 

Google Scholar 
Shive, K. L. et al. Ancient trees and modern wildfires: Declining resilience to wildfire in the highly fire-adapted giant sequoia. For. Ecol. Manag. 511, 120110 (2022).Article 

Google Scholar 
Searle, E. B., Chen, H. Y. H. & Paquette, A. Higher tree diversity is linked to higher tree mortality. Proc. Natl. Acad. Sci. U.S.A. 119, 1–7 (2022).Article 

Google Scholar 
Hart, S. J., Veblen, T. T., Eisenhart, K. S., Jarvis, D. & Kulakowski, D. Drought induces spruce beetle (Dendroctonus rufipennis) outbreaks across northwestern Colorado. Ecology 95, 930–939 (2014).Article 
PubMed 

Google Scholar 
Hart, S. J., Veblen, T. T. & Kulakowski, D. Do tree and stand-level attributes determine susceptibility of spruce-fir forests to spruce beetle outbreaks in the early 21st century? For. Ecol. Manag. 318, 44–53 (2014).Article 

Google Scholar 
Temperli, C. et al. Are density reduction treatments effective at managing for resistance or resilience to spruce beetle disturbance in the southern Rocky Mountains? For. Ecol. Manag. 334, 53–63 (2014).Article 

Google Scholar 
Six, D. L., Biber, E. & Long, E. Management for mountain pine beetle outbreak suppression: Does relevant science support current policy? Forests 5, 103–133 (2014).Article 

Google Scholar 
Black, S. H., Kulakowski, D., Noon, B. R. & Dellasala, D. A. Do bark beetle outbreaks increase wildfire risks in the central U.S. rocky mountains? Implications from recent research. Nat. Areas J. 33, 59–65 (2013).Article 

Google Scholar 
Oswalt, S. N., Smith, W. B., Miles, P. D. & Pugh, S. A. Forest Resources of the United States, 2017: A Technical Document Supporting the Forest Service 2020 RPA Assessment. Gen. Tech. Rep. WO-97 (US Department of Agriculture, Forest Service, 2019). https://doi.org/10.2737/WO-GTR-97.Book 

Google Scholar 
Cleland, D. et al. Terrestrial condition assessment for national forests of the USDA Forest Service in the continental US. Sustainability 9, 1–19 (2017).Article 

Google Scholar 
USDA Forest Service Forest Health Protection. Insect and Disease Detection Survey (IDS) data downloads. https://www.fs.usda.gov/foresthealth/applied-sciences/mapping-reporting/detection-surveys.shtml (2021). Accessed on 9 October 2021.Spruce, J. P. et al. Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks. Remote Sens. Environ. 115, 427–437 (2011).Article 
ADS 

Google Scholar 
Gomez, D. F., Ritger, H. M. W., Pearce, C., Eickwort, J. & Hulcr, J. Ability of remote sensing systems to detect bark beetle spots in the southeastern US. Forests 11, 1–10 (2020).Article 

Google Scholar 
Hanavan, R. P. et al. Supplementing the forest health national aerial survey program with remote sensing during the COVID-19 pandemic: Lessons learned from a collaborative approach. J. For. 120, 125–132 (2021).
Google Scholar 
Johnson, E. W. & Wittwer, D. Aerial detection surveys in the United States. Aust. For. 71, 212–215 (2008).Article 

Google Scholar 
Bright, B. C. et al. Using satellite imagery to evaluate bark beetle-caused tree mortality reported in aerial surveys in a mixed conifer forest in Northern Idaho, USA. Forests 11, 1–19 (2020).Article 

Google Scholar 
Coleman, T. W. et al. Accuracy of aerial detection surveys for mapping insect and disease disturbances in the United States. For. Ecol. Manag. 430, 321–336 (2018).Article 

Google Scholar 
Hicke, J. A., Xu, B., Meddens, A. J. H. & Egan, J. M. Characterizing recent bark beetle-caused tree mortality in the western United States from aerial surveys. For. Ecol. Manag. 475, 118402 (2020).Article 

Google Scholar 
Kosiba, A. M. et al. Spatiotemporal patterns of forest damage and disturbance in the northeastern United States: 2000–2016. For. Ecol. Manag. 430, 94–104 (2018).Article 

Google Scholar 
Meigs, G. W., Kennedy, R. E., Gray, A. N. & Gregory, M. J. Spatiotemporal dynamics of recent mountain pine beetle and western spruce budworm outbreaks across the Pacific Northwest Region USA. For. Ecol. Manag. 339, 71–86 (2015).Article 

Google Scholar 
Bechtold, W. A. & Patterson, P. L. The Enhanced Forest Inventory and Analysis Program—National Sampling Design and Estimation Procedures. Gen. Tech. Rep. SRS-80 (US Department of Agriculture, Forest Service, Southern Research Station, 2005). https://doi.org/10.2737/SRS-GTR-80.Book 

Google Scholar 
Randolph, K. D. C. et al. Past and present individual-tree damage assessments of the US national forest inventory. Environ. Monit. Assess. 193, 116 (2021).Article 
PubMed 

Google Scholar 
Kromroy, K. W., Juzwik, J., Castillo, P. & Hansen, M. H. Using forest service forest inventory and analysis data to estimate regional oak decline and oak mortality. North. J. Appl. For. 25, 17–24 (2008).Article 

Google Scholar 
Coulston, J. W., Edgar, C. B., Westfall, J. A. & Taylor, M. E. Estimation of forest disturbance from retrospective observations in a broad-scale inventory. Forests 11, 1298 (2020).Article 

Google Scholar 
Wilson, B. T., Lister, A. J. & Riemann, R. I. A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data. For. Ecol. Manag. 271, 182–198 (2012).Article 

Google Scholar 
Blackard, J. A. et al. Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information. Remote Sens. Environ. 112, 1658–1677 (2008).Article 
ADS 

Google Scholar 
Brosofske, K. D., Froese, R. E., Falkowski, M. J. & Banskota, A. A review of methods for mapping and prediction of inventory attributes for operational forest management. For. Sci. 60, 733–756 (2014).Article 

Google Scholar 
Lister, A. J. et al. Use of remote sensing data to improve the efficiency of national forest inventories: A case study from the United States national forest inventory. Forests 11, 1–41 (2020).Article 

Google Scholar 
USDA Forest Service Forest Health Protection. Individual Tree Species Parameter (ITSP) maps – GIS data downloads. https://www.fs.usda.gov/foresthealth/applied-sciences/mapping-reporting/indiv-tree-parameter-maps.shtml (2021). Accessed on 9 October 2021.Ellenwood, J. R., Krist, F. J. & Romero, S. A. National Individual Tree Species Atlas. FHTET-15-01 (US Department of Agriculture, Forest Service, Forest Health Technology Enterprise Team, 2015).
Google Scholar 
Krist, F. J. et al. National Insect and Disease Forest Risk Assessment. FHTET-14-01 (US Department of Agriculture, Forest Service, Forest Health Technology Enterprise Team, 2014).
Google Scholar 
Rulequest Inc. Cubist, release 2.07. https://www.rulequest.com/cubist-info.html (2011). Accessed on 15 July 2022.R Core Team. R: A language and environment for statistical computing. https://www.r-project.org (2021). Accessed on 4 March 2022.Esri Inc. ArcGIS Pro 2.8.0. https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview (2021). Accessed on 4 March 2022. More

Post, D. M. Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 83, 703–718 (2002).Article 

Google Scholar 
Fry, B. Stable Isotope Ecology (Springer, 2007).
Google Scholar 
Boon, P. I. & Bunn, S. E. Variations in the stable isotope composition of aquatic plants and their implications for food web analysis. Aquat. Bot. 48, 99–108 (1994).Article 

Google Scholar 
Kling, G. W., Fry, B. & O’Brien, W. J. Stable isotopes and planktonic trophic structure in arctic lakes. Ecology 73, 561–566 (1992).Article 

Google Scholar 
Nielsen, J. M., Clare, E. L., Hayden, B., Brett, M. T. & Kratina, P. Diet tracing in ecology: Method comparison and selection. Methods Ecol. Evol. 9, 278–291 (2018).Article 

Google Scholar 
Coulter, A. A., Swanson, H. K. & Goforth, R. R. Seasonal variation in resource overlap of invasive and native fishes revealed by stable isotopes. Biol. Invasions 21, 315–321 (2019).Article 

Google Scholar 
Jung, A. S., Van Der Veer, H. W., Van Der Meer, M. T. & Philippart, C. J. Seasonal variation in the diet of estuarine bivalves. PLoS One 14, e0217003 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Devlin, S. P., Vander Zanden, M. J. & Vadeboncoeur, Y. Depth-specific variation in carbon isotopes demonstrates resource partitioning among the littoral zoobenthos. Freshw. Biol. 58, 2389–2400 (2013).CAS 

Google Scholar 
Possamai, B., Vieira, J. P., Grimm, A. M. & Garcia, A. M. Temporal variability (1997–2015) of trophic fish guilds and its relationships with El Niño events in a subtropical estuary. Estuar. Coast. Shelf Sci. 202, 145–154 (2018).Article 
ADS 

Google Scholar 
Syvaranta, J., Hamalainen, H. & Jones, R. I. Within-lake variability in carbon and nitrogen stable isotope signatures. Freshw. Biol. 51, 1090–1102 (2006).Article 
CAS 

Google Scholar 
Janbu, A. D., Paasche, Ø. & Talbot, M. R. Paleoclimate changes inferred from stable isotopes and magnetic properties of organic-rich lake sediments in Arctic Norway. J. Paleolimnol. 46, 29 (2011).Article 
ADS 

Google Scholar 
Leng, M. et al. Late quaternary palaeoenvironmental reconstruction from Lakes Ohrid and Prespa (Macedonia/Albania border) using stable isotopes. Biogeosciences 7, 3109–3122 (2010).Article 
ADS 
CAS 

Google Scholar 
Jiang, Q., Shen, J., Liu, X., Zhang, E. & Xiao, X. A high-resolution climatic change since holocene inferred from multi-proxy of lake sediment in westerly area of China. Chin. Sci. Bull. 52, 1970–1979 (2007).Article 

Google Scholar 
Finlay, J. C. & Kendall, C. Stable isotope tracing of temporal and spatial variability in organic matter sources to freshwater ecosystems. Stable Isot. Ecol. Environ. Sci. 2, 283–333 (2007).Article 

Google Scholar 
Harvey, C. J. & Kitchell, J. F. A stable isotope evaluation of the structure and spatial heterogeneity of a Lake Superior food web. Can. J. Fish. Aquat. Sci. 57, 1395–1403 (2000).Article 
CAS 

Google Scholar 
Xu, D. et al. Spatial heterogeneity of food web structure in a large shallow eutrophic lake (Lake Taihu, China): Implications for eutrophication process and management. J. Freshw. Ecol. 34, 229–245 (2019).Article 
CAS 

Google Scholar 
Ruokonen, T., Kiljunen, M., Karjalainen, J. & Hämäläinen, H. Invasive crayfish increase habitat connectivity: A case study in a large boreal lake. Knowl. Manag. Aquat. Ecosyst. https://doi.org/10.1051/kmae/2013034 (2012).Article 

Google Scholar 
Veselý, L. et al. The crayfish distribution, feeding plasticity, seasonal isotopic variation and trophic role across ontogeny and habitat in a canyon-shaped reservoir. Aquat. Ecol. 54, 1169–1183 (2020).Article 

Google Scholar 
Kalff, J. Limnology: Inland Water Ecosystems Vol. 592 (Prentice Hall, 2002).
Google Scholar 
Polačik, M., Harrod, C., Blažek, R. & Reichard, M. Trophic niche partitioning in communities of African annual fish: Evidence from stable isotopes. Hydrobiologia 721, 99–106 (2014).Article 

Google Scholar 
Costalago, D., Navarro, J., Álvarez-Calleja, I. & Palomera, I. Ontogenetic and seasonal changes in the feeding habits and trophic levels of two small pelagic fish species. Mar. Ecol. Prog. Ser. 460, 169–181 (2012).Article 
ADS 

Google Scholar 
Matthews, B. & Mazumder, A. Consequences of large temporal variability of zooplankton δ15N for modeling fish trophic position and variation. Limnol. Oceanogr. 50, 1404–1414 (2005).Article 
ADS 
CAS 

Google Scholar 
Taipale, S., Kankaala, P., Tiirola, M. & Jones, R. I. Whole-lake dissolved inorganic 13C additions reveal seasonal shifts in zooplankton diet. Ecology 89, 463–474 (2008).Article 
PubMed 

Google Scholar 
Zohary, T., Erez, J., Gophen, M., Berman-Frank, I. & Stiller, M. Seasonality of stable carbon isotopes within the pelagic food web of Lake Kinneret. Limnol. Oceanogr. 39, 1030–1043 (1994).Article 
ADS 
CAS 

Google Scholar 
Stenroth, P. et al. Stable isotopes as an indicator of diet in omnivorous crayfish (Pacifastacus leniusculus): The influence of tissue, sample treatment, and season. Can. J. Fish. Aquat. Sci. 63, 821–831 (2006).Article 
CAS 

Google Scholar 
R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ (2021).Moore, J. W. & Semmens, B. X. Incorporating uncertainty and prior information into stable isotope mixing models. Ecol. Lett. 11, 470–480 (2008).Article 
PubMed 

Google Scholar 
Stock, B. C. & Semmens, B. X. Unifying error structures in commonly used biotracer mixing models. Ecology 97, 2562–2569 (2016).Article 
PubMed 

Google Scholar 
Irz, P., Laurent, A., Messad, S., Pronier, O. & Argillier, C. Influence of site characteristics on fish community patterns in French reservoirs. Ecol. Freshw. Fish 11, 123–136 (2002).Article 

Google Scholar 
Sutela, T., Aroviita, J. & Keto, A. Assessing ecological status of regulated lakes with littoral macrophyte, macroinvertebrate and fish assemblages. Ecol. Indic. 24, 185–192 (2013).Article 

Google Scholar 
Hunt, P. & Jones, J. The effect of water level fluctuations on a littoral fauna. J. Fish Biol. 4, 385–394 (1972).Article 

Google Scholar 
Kaster, J. & Jacobi, G. Benthic macroinvertebrates of a fluctuating reservoir. Freshw. Biol. 8, 283–290 (1978).Article 

Google Scholar 
Kraft, K. The effect of unnatural water level fluctuations on benthic invertebrates in Voyageurs National Park. Research⁄Resources Management Report MWR-12. US Department of the Interior, National Park Service. International Falls, Minnesota (1988).Glon, M., Larson, E. R. & Pangle, K. Comparison of 13C and 15N discrimination factors and turnover rates between congeneric crayfish Orconectes rusticus and O. virilis (Decapoda, Cambaridae). Hydrobiologia 768, 51–61 (2016).Article 
CAS 

Google Scholar 
Hesslein, R. H., Hallard, K. & Ramlal, P. Replacement of sulfur, carbon, and nitrogen in tissue of growing broad whitefish (Coregonus nasus) in response to a change in diet traced by δ34S, δ13C, and δ15N. Can. J. Fish. Aquat. Sci. 50, 2071–2076 (1993).Article 
CAS 

Google Scholar  More

McFall-Ngai, M. et al. Animals in a bacterial world, a new imperative for the life sciences. Proc. Natl. Acad. Sci. U.S.A. 110, 3229–3236 (2013).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hammer, T. J., Sanders, J. G. & Fierer, N. Not all animals need a microbiome. FEMS Microbiol. Lett. 366, fnz117 (2019).Article 
CAS 
PubMed 

Google Scholar 
Griffiths, S. M. et al. Host genetics and geography influence microbiome composition in the sponge Ircinia campana. J. Anim. Ecol. 88, 1684–1695 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Coleman-Derr, D. et al. Plant compartment and biogeography affect microbiome composition in cultivated and native Agave species. New Phytol. 209, 798–811 (2016).Article 
CAS 
PubMed 

Google Scholar 
Marzinelli, E. M. et al. Continental-scale variation in seaweed host-associated bacterial communities is a function of host condition, not geography. Environ. Microbiol. 17, 4078–4088 (2015).Article 
PubMed 

Google Scholar 
Wang, L., English, M. K., Tomas, F. & Mueller, R. S. Recovery and community succession of the Zostera marina Rhizobiome after transplantation. bioRxiv https://doi.org/10.1101/2020.04.20.052357 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Copeland, J. K., Yuan, L., Layeghifard, M., Wang, P. W. & Guttman, D. S. Seasonal community succession of the phyllosphere microbiome. Mol. Plant. Microbe. Interact. 28, 274–285 (2015).Article 
CAS 
PubMed 

Google Scholar 
Shi, S. et al. Successional trajectories of rhizosphere bacterial communities over consecutive seasons. MBio 6, e00746 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Shade, A., McManus, P. S. & Handelsman, J. Unexpected diversity during community succession in the apple flower microbiome. MBio 4, e00602 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Avena, C. V. et al. Deconstructing the bat skin microbiome: Influences of the host and the environment. Front. Microbiol. 7, 1753 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Rothschild, D. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210–215 (2018).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Glasl, B., Smith, C. E., Bourne, D. G. & Webster, N. S. Disentangling the effect of host-genotype and environment on the microbiome of the coral Acropora tenuis. PeerJ 7, e6377 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Weigel, B. L. & Erwin, P. M. Effects of reciprocal transplantation on the microbiome and putative nitrogen cycling functions of the intertidal sponge, Hymeniacidon heliophila. Sci. Rep. 7, 43247 (2017).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Fuhrman, J. A., Cram, J. A. & Needham, D. M. Marine microbial community dynamics and their ecological interpretation. Nat. Rev. Microbiol. 13, 133–146 (2015).Article 
CAS 
PubMed 

Google Scholar 
Ziegler, M. et al. Coral bacterial community structure responds to environmental change in a host-specific manner. Nat. Commun. 10, 3092 (2019).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Wagner, M. R. et al. Host genotype and age shape the leaf and root microbiomes of a wild perennial plant. Nat. Commun. 7, 12151 (2016).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kraft, N. J. B. et al. Community assembly, coexistence and the environmental filtering metaphor. Funct. Ecol. 29, 592–599 (2015).Article 

Google Scholar 
Weiher, E. & Keddy, P. A. The assembly of experimental wetland plant communities. Oikos 73, 323–335 (1995).Article 

Google Scholar 
Cavender-Bares, J., Kitajima, K. & Bazzaz, F. A. Multiple trait associations in relation to habitat differentiation among 17 Floridian oak species. Ecol. Monogr. 74, 635–662 (2004).Article 

Google Scholar 
Cavender-Bares, J., Ackerly, D. D., Baum, D. A. & Bazzaz, F. A. Phylogenetic overdispersion in Floridian oak communities. Am. Nat. 163, 823–843 (2004).Article 
CAS 
PubMed 

Google Scholar 
Webb, C. O. Exploring the Phylogenetic structure of ecological communities: An example for rain forest trees. Am. Nat. 156, 145–155 (2000).Article 
PubMed 

Google Scholar 
Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Syst. 33, 475–505 (2002).Article 

Google Scholar 
Kembel, S. W. et al. Relationships between phyllosphere bacterial communities and plant functional traits in a neotropical forest. Proc. Natl. Acad. Sci. U.S.A. 111, 13715–13720 (2014).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Burke, C., Steinberg, P., Rusch, D., Kjelleberg, S. & Thomas, T. Bacterial community assembly based on functional genes rather than species. Proc. Natl. Acad. Sci. U.S.A. 108, 14288–14293 (2011).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Martiny, J. B. H., Jones, S. E., Lennon, J. T. & Martiny, A. C. Microbiomes in light of traits: A phylogenetic perspective. Science https://doi.org/10.1126/science.aac9323 (2015).Article 
PubMed 

Google Scholar 
Goberna, M. & Verdú, M. Predicting microbial traits with phylogenies. ISME J. 10, 959–967 (2016).Article 
PubMed 

Google Scholar 
Duarte, C. M. The future of seagrass meadows. Environ. Conserv. 29, 192–206 (2002).Article 

Google Scholar 
Fonseca, M. S., Fisher, J. S., Zieman, J. C. & Thayer, G. W. Influence of the seagrass, Zostera marina L., on current flow. Estuar. Coast. Shelf Sci. 15, 351–364 (1982).Article 
ADS 

Google Scholar 
Fonseca, M. S., Kenworthy, W. J. & Thayer, G. W. A low cost transplanting procedure for sediment stabilization and habitat development using eelgrass (Zostera marina). Wetlands 2, 138–151 (1982).Article 

Google Scholar 
Moore, K. A. & Short, F. T. Zostera: Biology, ecology, and management. In Seagrasses: Biology, ecology and conservation (eds Larkum, A. W. D. et al.) 361–386 (Springer, 2006).
Google Scholar 
Fahimipour, A. K. et al. Global-scale structure of the eelgrass microbiome. Appl. Environ. Microbiol. 83, e03391-16 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Bengtsson, M. M. et al. Eelgrass leaf surface microbiomes are locally variable and highly correlated with epibiotic eukaryotes. Front. Microbiol. 8, 1312 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Cúcio, C., Engelen, A. H., Costa, R. & Muyzer, G. Rhizosphere microbiomes of European + seagrasses are selected by the plant, but are not species specific. Front. Microbiol. 7, 440 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Schenck, F. R., DuBois, K., Kardish, M. R., Stachowicz, J. J. & Hughes, A. R. The effect of warming on seagrass wasting disease depends on host genotypic identity and diversity. Ecology e3959 (2022).Beatty, D. S. et al. Predictable changes in eelgrass microbiomes with increasing wasting disease prevalence across 23° latitude in the Northeastern Pacific. mSystems 7, e0022422 (2022).Article 
PubMed 

Google Scholar 
Hughes, A. R., Stachowicz, J. J. & Williams, S. L. Morphological and physiological variation among seagrass (Zostera marina) genotypes. Oecologia 159, 725–733 (2009).Article 
ADS 
PubMed 

Google Scholar 
Randall Hughes, A. & Stachowicz, J. J. Seagrass genotypic diversity increases disturbance response via complementarity and dominance. J. Ecol. 99, 445–453 (2010).
Google Scholar 
Kamel, S. J., Hughes, A. R., Grosberg, R. K. & Stachowicz, J. J. Fine-scale genetic structure and relatedness in the eelgrass Zostera marina. Mar. Ecol. Prog. Ser. 447, 127–137 (2012).Article 
ADS 

Google Scholar 
Abbott, J. M., DuBois, K., Grosberg, R. K., Williams, S. L. & Stachowicz, J. J. Genetic distance predicts trait differentiation at the subpopulation but not the individual level in eelgrass Zostera marina. Ecol. Evol. 8, 7476–7489 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Sand-Jensen, K. Biomass, net production and growth dynamics in an eelgrass (Zostera marina L.) population in Vellerup Vig, Denmark. Ophelia 14, 185–201 (1975).Article 

Google Scholar 
Vacher, C. et al. The phyllosphere: Microbial jungle at the plant-climate interface. Annu. Rev. Ecol. Evol. Syst. 47, 1–24 (2016).Article 

Google Scholar 
Miazaki, A. S., Gastauer, M. & Meira-Neto, J. A. A. Environmental severity promotes phylogenetic clustering in campo rupestre vegetation. Acta Bot. Brasilica 29, 561–566 (2015).Article 

Google Scholar 
DuBois, K., Williams, S. L. & Stachowicz, J. J. Previous exposure mediates the response of eelgrass to future warming via clonal transgenerational plasticity. Ecology 101, e03169 (2020).Article 
PubMed 

Google Scholar 
Rüger, L. et al. Assembly patterns of the rhizosphere microbiome along the longitudinal root axis of maize (Zea mays L.). Front. Microbiol. 12, 614501 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Fitzpatrick, C. R. et al. Assembly and ecological function of the root microbiome across angiosperm plant species. Proc. Natl. Acad. Sci. U.S.A. 115, E1157–E1165 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Fitzgerald, D. B., Winemiller, K. O., Sabaj Pérez, M. H. & Sousa, L. M. Seasonal changes in the assembly mechanisms structuring tropical fish communities. Ecology 98, 21–31 (2017).Article 
PubMed 

Google Scholar 
Campbell, A. H., Marzinelli, E. M., Gelber, J. & Steinberg, P. D. Spatial variability of microbial assemblages associated with a dominant habitat-forming seaweed. Front. Microbiol. 6, 230 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Eriander, L., Infantes, E., Olofsson, M., Olsen, J. L. & Moksnes, P.-O. Assessing methods for restoration of eelgrass (Zostera marina L.) in a cold temperate region. J. Exp. Mar. Bio. Ecol. 479, 76–88 (2016).Article 

Google Scholar 
Zhou, Y. et al. Restoring eelgrass (Zostera marina L.) habitats using a simple and effective transplanting technique. PLoS ONE 9, e92982 (2014).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Galushko, A. & Kuever, J. Desulfocapsaceae. Bergey’s Manual of Systematics of Archaea and Bacteria 1–6 Preprint at https://doi.org/10.1002/9781118960608.fbm00332 (2021).Waite, D. W. et al. Proposal to reclassify the proteobacterial classes Deltaproteobacteria and Oligoflexia, and the phylum Thermodesulfobacteria into four phyla reflecting major functional capabilities. Int. J. Syst. Evol. Microbiol. 70, 5972–6016 (2020).Article 
CAS 
PubMed 

Google Scholar 
Knoblauch, C., Sahm, K. & Jørgensen, B. B. Psychrophilic sulfate-reducing bacteria isolated from permanently cold arctic marine sediments: description of Desulfofrigus oceanense gen. nov., sp. nov., Desulfofrigus fragile sp. nov., Desulfofaba gelida gen. nov., sp. nov., Desulfotalea psychrophila gen. nov., sp. nov. and Desulfotalea arctica sp. nov.. Int. J. Syst. Bacteriol. 49 Pt 4, 1631–1643 (1999).Article 
CAS 
PubMed 

Google Scholar 
Isaksen, M. F. & Teske, A. Desulforhopalus vacuolatus gen. nov., sp. nov., a new moderately psychrophilic sulfate-reducing bacterium with gas vacuoles isolated from a temperate estuary. Arch. Microbiol. 166, 160–168 (1996).Article 
CAS 

Google Scholar 
Song, J., Hwang, J., Kang, I. & Cho, J.-C. A sulfate-reducing bacterial genus, Desulfosediminicola gen. nov., comprising two novel species cultivated from tidal-flat sediments. Sci. Rep. 11, 19978 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Trevelline, B. K., Fontaine, S. S., Hartup, B. K. & Kohl, K. D. Conservation biology needs a microbial renaissance: a call for the consideration of host-associated microbiota in wildlife management practices. Proc. Biol. Sci. 286, 20182448 (2019).PubMed 
PubMed Central 

Google Scholar 
Christian, N., Whitaker, B. K. & Clay, K. Microbiomes: Unifying animal and plant systems through the lens of community ecology theory. Front. Microbiol. 6, 869 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Zieman, J. C. Productivity in seagrasses: Methods and rates. In Handbook of Seagrass Biology: An ecosystem perspective (eds Phillips, R. C. & McRoy, C. P.) 87–116 (Garland STPM Press, 1980).
Google Scholar 
Dennison, W. C. Leaf production. Seagrass research methods, UNESCO, Paris 77–79 (1990).Walters, W. et al. Improved bacterial 16S rRNA gene (V4 and V4–5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. mSystems 1, e00009-15 (2016).Article 
PubMed 

Google Scholar 
Comeau, A. M., Douglas, G. M. & Langille, M. G. I. Microbiome Helper: A custom and streamlined workflow for microbiome research. mSystems 2, e00127-16 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).Article 
CAS 
PubMed 

Google Scholar 
Wright, E. S. DECIPHER: Harnessing local sequence context to improve protein multiple sequence alignment. BMC Bioinform. 16, 322 (2015).Article 

Google Scholar 
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2-approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Britton, T., Anderson, C. L., Jacquet, D., Lundqvist, S. & Bremer, K. Estimating divergence times in large phylogenetic trees. Syst. Biol. 56, 741–752 (2007).Article 
PubMed 

Google Scholar 
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Paradis, E. & Schliep, K. ape 50: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).Article 
CAS 
PubMed 

Google Scholar 
Silverman, J. D., Washburne, A. D., Mukherjee, S. & David, L. A. A phylogenetic transform enhances analysis of compositional microbiota data. Elife 6, 1–20 (2017).Article 

Google Scholar 
McMurdie, P. J. & Holmes, S. Waste not, want not: Why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Webb, C. O., Ackerly, D. D. & Kembel, S. W. Phylocom: Software for the analysis of phylogenetic community structure and trait evolution. Bioinformatics 24, 2098–2100 (2008).Article 
CAS 
PubMed 

Google Scholar 
Russel, J. Russel88/MicEco: v0.9.15. (2021). 10.5281/zenodo.4733747.Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).Article 
CAS 
PubMed 

Google Scholar 
Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Kahle, D. & Wickham, H. Ggmap: Spatial visualization with ggplot2. R J. 5, 144 (2013).Article 

Google Scholar  More