Ecology

Sample collectionA survey was conducted among the residents of nine counties in Kenya (Mombasa, Kisumu, Machakos, Nairobi, Makueni, Bomet, Kericho, Kiambu, and Narok) and GPS location coordinates were recorded and later used to build the predictive model (“Infestation dynamics of bedbugs in residential communities” section). These counties represent diversity in cultural practices, livelihood strategies (such as fishing, tourism, farming), and infrastructure development. Also, they comprise different altitudes above sea level, temperatures, and differing in average annual rainfall.Samples identification using morphological identification keysIn each county where the survey was conducted, bedbug samples was taken and preserved in ethanol 70% for morphological identification. Cimex belonging to Cimicidae family is the common genus adapted to human environment and reported throughout the world and comprising species such as Cimex lectularius and C. hemipterus that are hematophagous mainly feeding on human blood5. The key morphological features used in identifying bedbugs include: (1) the head has a labrum that appears as a free sclerite at the extreme anterior margin, ecdysial lines form a broad V, eyes project from the sides composed of several facets and the antennae are 4-segmented, (2) thorax is subdivided into prothorax, mesothorax and metathorax, (3) legs have all other normal parts except pulvilli and arolia, tarsus is 3-segmented with 2 simple claws, (4) the abdomen has 11 more-or-less segmented recognizable segments, 7 pairs of spiracles borne on the second to eighth segments, hosts the genital structures, paramere in males and mesospermalege in females45. Bedbug specimen morphological features were examined using Leica EZ24 HD dissecting microscope (Leica Microsystems, UK) and photos documented using the associated software.Survey for household’s knowledge and perceptions on bedbugsThis study was a community-based cross-sectional survey conducted from November–December 2020 with respect of the rules/guidlines introduced by the Ministry of Health to contain the COVID-19 pandemic in Kenya (wearing mask, social distance, washing hand, etc.). It was based on a stratified, systematic random sampling where 100 respondents were selected from each county.A total number of 900 respondents were randomly selected and the household head or the representative showing willingness and consent was interviewed face-to-face. The interview was conducted using a semi-structured questionnaire prepared in the English language (Appendix A). The questionnaire was translated into the local native language (Kiswahili) to avoid biasness and improve the understanding between the enumerator and the respondent. Prior to the commencement of the survey and authentic data collection, a pre-testing exercise was performed by training enumerators on a similar socio-demographic pattern. This was useful for improving the quality of data, ensuring validity, familiarizing the enumerators with the questionnaire, and data handling.The information collected using the semi-structured questionnaire included residents’ socio-economic profiles, knowledge, and perceptions on the pest, bedbug incidence, and management practices. The socio-economic profile factors addressed in the survey comprised gender, age, education, access to basic social amenities, and household size. The study also prioritized the financial consequences, the severity of the bites, perceptions of respondents on the pest, and management practices for its control.Survey data were checked for errors, completeness, summarized, and entered in Microsoft-Excel. It was then cleaned and transferred to Statistical Package for Social Science (SPSS) version 25 software (IBM Corp., Armonk, NY) for purposes of descriptive statistics (means and percentages).In contrast, in instances where more than one reason was given for a single question, percentages were calculated based on each group of similar responses. Chi-square was performed to determine the differences regarding socio-demographic characteristics, knowledge, and perceptions on bedbugs and control practices. Additionally, data were disaggregated by gender and age categories to understand the existing differences among the various respondent categories. Besides, F-test statistics was performed on the ages of respondents to determine the mean, standard deviation and statistical significance. The level of significance was considered when the p-value was below 5%.Infestation dynamics model of bedbugModel simulation assumptionsHouses infestation dynamics was studied following Susceptible-Infested-Treatment (SIT) model46. Therefore, houses in the community are classified into three groups: susceptible, infested or treated. Within a house, bedbug population dynamics was ignored, while it was considered from one house to another where infested houses have some potential to spread the infestation to other houses in the community. A population of bedbugs in an infested house has some probability per unit of time of becoming extinct either naturally or after treatment. In the infestation dynamics, the rate of house infestation depends on the number of infested houses, the movement of people from one house to another and the proportion of treated houses in the community. We assume that infested houses (I) spread the infestation at the rate β and only a fraction S/N of the houses is susceptible (S) to infestation. Infested houses become extinct at a certain rate known as rate γ. Infested houses are treated at the rate τ and the protection conferred is lost at the rate α. Ordinary differential equation developed to study SIT model were used in this study46. All the models used have the generic formulations displayed below:$$frac{dS}{dt}=frac{beta }{N}SI+gamma I+alpha T$$
(1)
$$frac{dI}{dt}=frac{beta }{N}SI-(gamma +tau )I$$
(2)
$$frac{dT}{dt}=tau I-alpha T$$
(3)
where β  > 0, τ  > 0, α ≥ 0 and γ  > 0. The total population size is N = S(t) + I(t) + t(t). The initial conditions satisfy at S(0)  > 0, I(0)  > 0, T(0) ≥ 0 and S(0) + I(0) = N, where N is the constant total population size, dN/dt = 0.Infestation dynamics models implementationThe method used to implement the infestation dynamics model of the pest is based on the system thinking approach with its archetypes [Causal Loop Diagram (CLD), Reinforcing (R) and Balancing (B)] by a mental and holistic conceptual framework. This is important for mapping how the variables, issues, and processes influence each other in the complex interactions of bedbugs within and between houses and their impacts. Despite these archetypes being qualitative, they are necessary for elucidating and disclosing the basic feedback configurations that occur in houses and their environs when infested with pests like bedbugs. A dynamic model was generated by converting the causal loop diagram (CLD) obtained using stocks, flows, auxiliary links, and clouds. Consequently, these in turn were translated into coupled differential equations for simulations.The SIT model was translated into causal loop diagram where arrows show the cause-effect relations where positive sign indicates direct proportionality of cause and effect while negative sign shows inverse proportionality relations, and two different scenarios have been assessed: (1) homogeneous houses where there is a single community of houses of the same quality, and (2) heterogeneous houses where there is a community of good and bad houses. Ancient houses presenting slits/fissures with less cleanliness and filled with old or secondhand furniture at low grade are considered bad houses as they may sustain high level of bedbug infestation; and new houses don’t provide well enough conditions for bedbug population to survive, and they are called in the model good houses47. Bad houses are considered to act as sources while good houses act as sinks, but all together are randomly distributed where each house has the same probability to contact good or bad houses.In the scenarios of homogeneous houses, the causal loop diagram (Fig. 7) has two feedback loops: (a) one positive, as the number of infested houses increases, the probability to get susceptible houses infested also increases resulting in infested houses increase; (b) one negative, as the infested houses increases, the treated houses increase resulting in susceptible houses decrease. The causal loop diagram is displayed in Fig. 7A while Fig. 7B showed the stocked and flows diagram and axillary variables obtained from causal loop diagram.Figure 7Susceptible-Infested-Treatment (SIT) model translated into causal loop diagram (A) and stock and flow diagram (B) for homogeneous houses and causal loop diagram (C) and stock and flow diagram (D) for heterogeneous houses in the community.Full size imageSusceptible, infested, and treated houses are stocks in the system, representing the number of houses susceptible, infested, and treated, respectively at a given point of time. The rates represent in and out-flows of the diagram. Auxiliary and constants that drive the behavior of the system were connected using information arrows within them and flows and stocks to represent the relations among variables in terms of equations.In the scenarios of heterogeneous houses, the causal loop diagram (Fig. 7C) comes with the two previous feedback loops but for each category of house. In addition, there is a fifth feedback loop that connect bad house to good house and vice versa.Therefore, as the infested bad houses increase, the probability to infest good houses increases. The more they are exposed the more they get infested. In turn, as the infested good houses increase, the chance to infest susceptible bad houses increases and the more they are exposed, the more they get infested, resulting in the increase of infested bad houses. The stocks and flows diagram of each of the two categories of houses occurred with interconnexion relationships between the two categories (Fig. 7D).Models’ simulationsThe survey data (“Bedbug Genus identification” section) on prevalence, knowledge, perceptions and self-reported; in addition, the respondents’ reported control mechanisms and their average time of effectiveness (Appendix B, Table S1) were used for model simulations. The different control methods reported were reclassified in three control approaches: chemical control, other control methods (including exposure to direct sunlight, use of hot water, painting, application of diesel, paraffin and wood ash, use of Aloe Vera extract and Herbs), and combination of chemical and other control methods. All the models commodities and units were checked before performing the simulations. Simulation and implementation of the models were done using Vensim PLP 8.1 platform (Ventana systems, Harvard, USA). It consists of a graphical environment that usually permits drawing of Causal Loop Diagram (CLD), stocks, flow diagrams and to carry out simulations. After we simulated the infestation dynamics under the two scenarios, we explored the effect of the different control methods.Spatial distribution analysis of bedbugs using MaxEnt modelEnvironmental data for MaxEntThe environmental variables used as the other maxent input were obtained by deriving bioclimatic, land cover, and elevation data. Bioclimatic variables and elevation (Digital Elevation Model; DEM) data were obtained from the Global Climate Data official website, Worldclim (http://www.worldclim.org/bioclim.htm)48 including 19 bioclimatic variables (Appendix B, Table S2). The land cover data were downloaded from the Global Land Cover Facility (GLCF).In order to reduce collinearity between predictors, a collinearity test was performed on all the variables by filtering them according to the following steps36: firstly, the MaxEnt model was run using the distribution data of bedbugs and 19 bioclimatic variables to obtain the percent contribution of each variable to the preliminary prediction results. Secondly, following the generation of the percentage contribution of all the variables, we then imported all distribution points in Arc-GIS and extracted the attribute values of the 19 variables. Furthermore, the “virtual species” package49 in R-software (R Foundation for Statistical Computing, Vienna, Australia) was used to explore the extracted variables’ clusters spatial correlation using Pearson’s correlation coefficient and the cluster tree (Fig. 8). Thus, the final number of predictor variables after screening was 5 establishing the potential geographical distribution of bedbug, which includes Temperature Seasonality (bio4), Precipitation of Driest Month (bio14), Temperature Annual Range (bio7), Precipitation of Driest Quarter (bio17) and Precipitation of Warmest Quarter (bio18) (Appendix B, Table S2). The land cover was considered because studies have shown its importance on insect spatial distribution50,51,52 and it was setled as a categorical variable53. Elevation was selected as variable because it greatly influences species’ occurrence and dispersal by affecting the temperature, precipitation, vegetation, and sun characteristics (direction, intensity, etc.) on the earth’s surface54,55,56. The study variables had different resolutions and were therefore, resampled to 1 km. The variables were clipped to Kenya and Africa boundaries and converted to ASCII (Stands for “American Standard Code for Information Interchange”) format using the ‘raster’ package49 in R statistical software (R Foundation for Statistical Computing, Vienna, Australia).Figure 8Key model predictor variables.Full size imageDistribution modelling in Kenya and AfricaIn our study, we used the maximum entropy distribution modelling method. This is because it has been recommended to have the ability to perform best and remain effective despite the use of small sample size relative to the other modelling methods57.Our selected bioclimatic variables (5) and occurrence/prevalence data for bedbugs were then imported into MaxEnt model and the options of ‘Create response curves’ and ‘Do jackknife’ were selected to measure variable importance’ options. The model output file was selected as ‘Logistic’, the commonly used approach is the random portioning of distribution datasets into ‘training’, and ‘test’ sets57,58. MaxEnt model was run with a total number of 5000 iterations and five replicates for better convergence of the model and rescaled within the range of 0–1000 suitability scores using ‘raster’ package49 in R statistical software (R Foundation for Statistical Computing, Vienna, Australia).The modelling performance/MaxEnt accuracy was evaluated by choosing the area under the receiver operating characteristics (ROC) curve (AUC) as the estimation index. This was important for the calibration and validation of the robustness of MaxEnt model evaluation. Furthermore, the area under the ROC curve (AUC) was necessary as an additional precision analysis59. The range of AUC values greater than 0.7 was considered a fair model performance, while those greater than 0.9 indicated that the model was considered an excellent model performance. Therefore, by considering the AUC values, the excellently performing model was selected to analyze the suitability of bedbugs in Kenya and Africa59,60,61,62.The ASCII format output was then imported into QGIS 3.10.2 (using the QGIS 3.10.2 software, https://qgis.org/downloads/), following its conversion into a raster format file using R software. This was useful for the classification and visualization of the distribution area63,64. The potential suitable distribution of bedbugs was extracted using the Kenyan and African maps. At the same time, Jenks’ natural breaks were also used to reclassify and classify the suitability into five categories, namely: unsuitable (P  More

C40 Cities. 700+ cities in 53 countries now committed to halve emissions by 2030 and reach net zero by 2050. C40 Cities https://www.c40.org/news/cities-committed-race-to-zero/ (2021).Watts, M. Cities spearhead climate action. Nat. Clim. Change 7, 537–538 (2017).
Google Scholar 
Brownlie, W. J. et al. Global actions for a sustainable phosphorus future. Nat. Food 2, 71–74 (2021).CAS 

Google Scholar 
El Wali, M., Golroudbary, S. R. & Kraslawski, A. Circular economy for phosphorus supply chain and its impact on social sustainable development goals. Sci. Total Environ. 777, 146060 (2021).CAS 

Google Scholar 
Bai, X. et al. Defining and advancing a systems approach for sustainable cities. Curr. Opin. Environ. Sustain. 23, 69–78 (2016).
Google Scholar 
De Boer, M. A., Wolzak, L. & Slootweg, J. C. Phosphorus: reserves, production, and applications. in Phosphorus Recovery and Recycling. (eds. Ohtake, H. & Tsuneda, S.) 75–100 (Springer, 2019).Brownlie, W. J. et al. Chapter 2. Phosphorus reserves, resources and uses. In Our Phosphorus Future (eds. Brownlie, W. J., Sutton, M. A., Heal, K. V., Reay, D. S. & Spears, B. M.) (UK Centre for Ecology & Hydrology, 2022). https://doi.org/10.13140/RG.2.2.25016.83209.Chow, E. China issues phosphate quotas to rein in fertiliser exports – analysts. Reuters (2022).Klesty, V. Global food supply at risk from Russian invasion of Ukraine, Yara says. Reuters (2022).Dumas, M., Frossard, E. & Scholz, R. W. Modeling biogeochemical processes of phosphorus for global food supply. Chemosphere 84, 798–805 (2011).CAS 

Google Scholar 
Cordell, D., Turner, A. & Chong, J. The hidden cost of phosphate fertilizers: mapping multi-stakeholder supply chain risks and impacts from mine to fork. Glob. Change Peace Secur. 27, 1–21 (2015).
Google Scholar 
Metson, G. S., Bennett, E. M. & Elser, J. J. The role of diet in phosphorus demand. Environmental Research Letters 7, 044043 (2012).
Google Scholar 
Oita, A., Wirasenjaya, F., Liu, J., Webeck, E. & Matsubae, K. Trends in the food nitrogen and phosphorus footprints for Asia’s giants: China, India, and Japan. Resour. Conserv. Recycl. 157, 104752 (2020).
Google Scholar 
Chen, M. & Graedel, T. E. A half-century of global phosphorus flows, stocks, production, consumption, recycling, and environmental impacts. Glob. Environ. Chang. 36, 139–152 (2016).
Google Scholar 
Johnes, P. J. et al. Chapter 5. Phosphorus and water quality. in Our Phosphorus Future (eds. Brownlie, W. J., Sutton, M. A., Heal, K. V., Reay, D. S. & Spears, B. M.) (UK Centre for Ecology & Hydrology, 2022). https://doi.org/10.13140/RG.2.2.14950.50246.Dodds, W. K. et al. Eutrophication of US freshwaters: analysis of potential economic damages. Environ. Sci. Technol. 43, 12–19 (2008).
Google Scholar 
Watson, S. B. et al. The re-eutrophication of Lake Erie: Harmful algal blooms and hypoxia. Harmful Algae 56, 44–66 (2016).CAS 

Google Scholar 
Rabalais, N. N. & Turner, R. E. Gulf of Mexico Hypoxia: Past, Present, and Future. Limnol. Oceanogr. Bull. 28, 117–124 (2019).
Google Scholar 
Carstensen, J. & Conley, D. J. Baltic Sea Hypoxia Takes Many Shapes and Sizes. Limnol. Oceanog. Bull. 28, 125–129 (2019).
Google Scholar 
Kanter, D. R. & Brownlie, W. J. Joint nitrogen and phosphorus management for sustainable development and climate goals. Environ. Sci. Policy 92, 1–8 (2019).CAS 

Google Scholar 
Hamilton, D. P., Salmaso, N. & Paerl, H. W. Mitigating harmful cyanobacterial blooms: strategies for control of nitrogen and phosphorus loads. Aquat. Ecol. 50, 351–366 (2016).CAS 

Google Scholar 
Brownlie, W. J. et al. Chapter 9. Towards our phosphorus future. In Our Phosphorus Future (eds. Brownlie, W. J., Sutton, M. A., Heal, K. V., Reay, D. S. & Spears, B. M.) (UK Centre for Ecology & Hydrology, 2022). https://doi.org/10.13140/RG.2.2.16995.22561.MacDonald, G. K. et al. Guiding phosphorus stewardship for multiple ecosystem services. Ecosyst. Health Sustain. 2, e01251 (2016).
Google Scholar 
Withers, P. J. A. et al. Stewardship to tackle global phosphorus inefficiency: The case of Europe. Ambio 44, 193–206 (2015).CAS 

Google Scholar 
Withers, P. J. A. et al. Towards resolving the phosphorus chaos created by food systems. Ambio 49, 1076–1089 (2020).CAS 

Google Scholar 
Withers, P. J. A. Closing the phosphorus cycle. Nat. Sustain. 2, 1001–1002 (2019).
Google Scholar 
Langhans, C., Beusen, A. H. W., Mogollón, J. M. & Bouwman, A. F. Phosphorus for Sustainable Development Goal target of doubling smallholder productivity. Nat. Sustain. 5, 57–63 (2022).
Google Scholar 
Kuss, P. & Nicholas, K. A. A dozen effective interventions to reduce car use in European cities: Lessons learned from a meta-analysis and transition management. Case Stud. Transp. Policy. 10, 1494–1513 (2022).
Google Scholar 
Hobbie, S. E. et al. Contrasting nitrogen and phosphorus budgets in urban watersheds and implications for managing urban water pollution. Proc. Natl. Acad. Sci. USA 114, E4116–E4116 (2017).
Google Scholar 
Seto, K. C. et al. From low- to net-zero carbon cities: the next global agenda. Annu. Rev. Environ. Resour. 46, 377–415 (2021).
Google Scholar 
Zhang, Y. Urban metabolism: A review of research methodologies. Environ. Pollut. 178, 463–473 (2013).CAS 

Google Scholar 
Kissinger, M. & Stossel, Z. An integrated, multi-scale approach for modelling urban metabolism changes as a means for assessing urban sustainability. Sustain. Cities Soc. 67, 102695 (2021).
Google Scholar 
Li, H. & Kwan, M.-P. Advancing analytical methods for urban metabolism studies. Resour. Conserv. Recycl. 132, 239–245 (2018).
Google Scholar 
Goldstein, B., Birkved, M., Quitzau, M.-B. & Hauschild, M. Quantification of urban metabolism through coupling with the life cycle assessment framework: concept development and case study. Environ. Res. Lett. 8, 035024 (2013).CAS 

Google Scholar 
Kovac, A. et al. Global Protocol for Community-Scale Greenhouse Gas Inventories— An Accounting and Reporting Standard for Cities Version 1.1. 190 https://ghgprotocol.org/greenhouse-gas-protocol-accounting-reporting-standard-cities.Rogelj, J., Geden, O., Cowie, A. & Reisinger, A. Net-zero emissions targets are vague: three ways to fix. Nature 591, 365–368 (2021).CAS 

Google Scholar 
Wiedmann, T. et al. Three-scope carbon emission inventories of global cities. J. Ind. Ecol. 25, 735–750 (2021).CAS 

Google Scholar 
Metson, G. S. et al. Urban phosphorus sustainability: Systemically incorporating social, ecological, and technological factors into phosphorus flow analysis. Environ. Sci. Policy 47, 1–11 (2015).CAS 

Google Scholar 
Harseim, L., Sprecher, B. & Zengerling, C. Phosphorus governance within planetary boundaries: the potential of strategic local resource planning in The Hague and Delfland, The Netherlands. Sustainability 13, 10801 (2021).CAS 

Google Scholar 
Coutard, O. & Florentin, D. Resource ecologies, urban metabolisms, and the provision of essential services. J. Urban Technol. 29, 49–58 (2022).
Google Scholar 
UDG at COP26 | Urban Design Events. Urban Design Group https://www.udg.org.uk/events/2021/udg-cop26 (2021).Ramaswami, A., Russell, A. G., Culligan, P. J., Sharma, K. R. & Kumar, E. Meta-principles for developing smart, sustainable, and healthy cities. Science 352, 940–943 (2016).CAS 

Google Scholar 
McPhearson, T. et al. A social-ecological-technological systems framework for urban ecosystem services. One Earth 5, 505–518 (2022).
Google Scholar 
McPhearson, T., Haase, D., Kabisch, N. & Gren, Å. Advancing understanding of the complex nature of urban systems. Ecol. Indic. 70, 566–573 (2016).
Google Scholar 
Metson, G. S. et al. Socio-environmental consideration of phosphorus flows in the urban sanitation chain of contrasting cities. Regional Environmental Change 18, 1387–1401 (2018).
Google Scholar 
Iwaniec, D. M., Metson, G. S. & Cordell, D. P-FUTURES: Towards urban food & water security through collaborative design and impact. Curr. Opin. Environ. Sustain. 20, 1–7 (2016).
Google Scholar 
Bulkeley, H. et al. Urban living laboratories: Conducting the experimental city? Eur. Urban. Reg. Stud. 26, 317–335 (2019).
Google Scholar 
Beukers, E. & Bertolini, L. Learning for transitions: An experiential learning strategy for urban experiments. Environ. Innov. Soc. Transit. 40, 395–407 (2021).
Google Scholar 
Ramaswami, A. et al. Carbon analytics for net-zero emissions sustainable cities. Nat. Sustain. 4, 460–463 (2021).
Google Scholar 
Petit-Boix, A., Apul, D., Wiedmann, T. & Leipold, S. Transdisciplinary resource monitoring is essential to prioritize circular economy strategies in cities. Environ. Res. Lett. 17, 021001 (2022).
Google Scholar 
WWAP. Wastewater: The Untapped Resource. https://www.unwater.org/publications/un-world-water-development-report-2017 (2017).van Puijenbroek, P. J. T. M., Beusen, A. H. W. & Bouwman, A. F. Global nitrogen and phosphorus in urban waste water based on the Shared Socio-economic pathways. J. Environ. Manage. 231, 446–456 (2019).
Google Scholar 
Kovacs, A. & Zavadsky, I. Success and sustainability of nutrient pollution reduction in the Danube River Basin: recovery and future protection of the Black Sea Northwest shelf. Water Int. 46, 176–194 (2021).
Google Scholar 
Trimmer, J. T. & Guest, J. S. Recirculation of human-derived nutrients from cities to agriculture across six continents. Nat. Sustain. 1, 427–435 (2018).
Google Scholar 
Powers, S. M. et al. Global opportunities to increase agricultural independence through phosphorus recycling. Earths Future 7, 370–383 (2019).
Google Scholar 
Metson, G. S., Cordell, D., Ridoutt, B. & Mohr, S. Mapping phosphorus hotspots in Sydney’s organic wastes: a spatially-explicit inventory to facilitate urban phosphorus recycling. J. Urban Ecol. 4, 1–19 (2018).
Google Scholar 
Hu, Y., Sampat, A. M., Ruiz-Mercado, G. J. & Zavala, V. M. Logistics Network Management of Livestock Waste for Spatiotemporal Control of Nutrient Pollution in Water Bodies. ACS Sustain. Chem. Eng. 7, 18359–18374 (2019).CAS 

Google Scholar 
Mayer, B. K. et al. Total value of phosphorus recovery. Environ. Sci. Technol. 50, 6606–6620 (2016).CAS 

Google Scholar 
van Hessen, J. An Assessment of Small-Scale Biodigester Programmes in the Developing World: The SNV and Hivos Approach. (Vrije Universiteit Amsterdam, 2014).Harder, R., Wielemaker, R., Larsen, T. A., Zeeman, G. & Öberg, G. Recycling nutrients contained in human excreta to agriculture: Pathways, processes, and products. Crit. Rev. Environ. Sci. Technol. 49, 695–743 (2019).
Google Scholar 
Metson, G. S. et al. Chapter 8. Consumption: the missing link towards phosphorus security. In Our Phosphorus Future (eds. Brownlie, W. J., Sutton, M. A., Heal, K. V., Reay, D. S. & Spears, B. M.) (UK Centre for Ecology & Hydrology, 2022). https://doi.org/10.13140/RG.2.2.36498.73925.Qiao, M., Zheng, Y. M. & Zhu, Y. G. Material flow analysis of phosphorus through food consumption in two megacities in northern China. Chemosphere 84, 773–778 (2011).CAS 

Google Scholar 
Forber, K. J., Rothwell, S. A., Metson, G. S., Jarvie, H. P. & Withers, P. J. A. Plant-based diets add to the wastewater phosphorus burden. Environ. Res. Lett. 15, 094018 (2020).CAS 

Google Scholar 
UN Population Division. The World’s cities in 2018. https://digitallibrary.un.org/record/3799524 (2018).Klöckner, C. A. A comprehensive model of the psychology of environmental behaviour-A meta-analysis. Glob. Environ. Change 23, 1028–1038 (2013).
Google Scholar 
Nyborg, K. et al. Social norms as solutions. Science 354, 42–43 (2016).CAS 

Google Scholar 
Vermeir, I. & Verbeke, W. Sustainable Food Consumption: Exploring the Consumer “Attitude – Behavioral Intention” Gap. J. Agric. Environ. Ethics 19, 169–194 (2006).
Google Scholar 
Ullström, S., Stripple, J. & Nicholas, K. A. From aspirational luxury to hypermobility to staying on the ground: changing discourses of holiday air travel in Sweden. J. Sustain. Tour. https://doi.org/10.1080/09669582.2021.1998079 (2021).Morris, T. H. Experiential learning—a systematic review and revision of Kolb’s model. Interact. Learn. Environ. 28, 1064–1077 (2020).
Google Scholar 
Metson, G. S. & Bennett, E. M. Facilitators & barriers to organic waste and phosphorus re-use in Montreal. Elementa 3, 000070 (2015).
Google Scholar 
Winkler, B., Maier, A. & Lewandowski, I. Urban gardening in germany: cultivating a sustainable lifestyle for the societal transition to a bioeconomy. Sustainability 11, 801 (2019).
Google Scholar 
Kim, J. E. Fostering behaviour change to encourage low-carbon food consumption through community gardens. Int. J. Urban Sci. 21, 364–384 (2017).
Google Scholar 
Fuhr, H., Hickmann, T. & Kern, K. The role of cities in multi-level climate governance: local climate policies and the 1.5 °C target. Curr. Opin. Environ. Sustain. 30, 1–6 (2018).
Google Scholar 
Steffen, W. et al. Planetary boundaries: Guiding human development on a changing planet. Science 347, 1259855 (2015).
Google Scholar 
Santos, A. F., Almeida, P. V., Alvarenga, P., Gando-Ferreira, L. M. & Quina, M. J. From wastewater to fertilizer products: Alternative paths to mitigate phosphorus demand in European countries. Chemosphere 284, 131258 (2021).CAS 

Google Scholar 
UNFCCC. Race To Zero Campaign. https://unfccc.int/climate-action/race-to-zero-campaign.Locsin, J. A., Hood, K. M., Doré, E., Trueman, B. F. & Gagnon, G. A. Colloidal lead in drinking water: Formation, occurrence, and characterization. Crit. Rev. Environ. Sci. Technol. https://doi.org/10.1080/10643389.2022.2039549 (2022).Li, Y. et al. The role of freshwater eutrophication in greenhouse gas emissions: A review. Sci. Total Environ. 768, 144582 (2021).CAS 

Google Scholar 
Gong, H. et al. Synergies in sustainable phosphorus use and greenhouse gas emissions mitigation in China: Perspectives from the entire supply chain from fertilizer production to agricultural use. Sci. Total Environ. 838, 155997 (2022).CAS 

Google Scholar  More

Bacterial density and growth potential in the rearing water were related to the microbial carrying capacityQuantifying the bacterial density in each tank verified that we obtained a higher bacterial load in the systems with added organic material. The bacterial density was, on average, 7.8× higher in the systems with high compared to low bacterial carrying capacity. This difference was particularly evident at 2 (34.8×, Kruskal–Wallis p = 0.0008) and 9 DPH (9.1×, Kruskal–Wallis p = 0.0007) (Fig. 1). The bacterial density increased throughout the experiment for the tanks with low microbial carrying capacity (treatment group MMS−, FTS−), reflecting increased larval feeding and defecation. Contrastingly, the bacterial density was relatively stable over time in the MMS+ treatment and even decreased over time in the FTS+ treatment. When averaging the densities at 11 and 15 DPH within each rearing treatment, we observed that the ‘MMS+ to FTS+’ had a considerable difference in the bacterial density between incoming and rearing water (24.2×). In contrast, this difference was below 8.2× in all other treatment tanks. Such differences in density indicated that some communities were below the microbial carrying capacity of the systems. We thus investigated the growth potential to determine if carrying capacity was reached in the rearing water.Figure 1Bacterial density (million bacterial cells mL−1) at various days post-hatching (DPH) in incoming and rearing tank water. Note that the y-axis is log scaled. Colours indicate the rearing treatment, and shape signifies rearing (filled circle) and incoming water (filled triangle).Full size imageThe bacterial net growth potential in the intake and rearing water was quantified as the number of cell doublings after incubation for 3 days11. Generally, the FTS− and MMS− rearing water had net growth potential with an average of 0.2 and 0.1, respectively (Supplementary Fig. 2). In contrast, the rearing water of the FTS+ and MMS+ had a negative net growth potential with averages of −0.2 and −0.06, respectively. In the case of negative net growth potential, the bacterial density decreased during the incubation. A negative net growth potential suggested that the rearing water bacterial communities were at the tank’s microbial carrying capacity at the time of sampling. Thus, the bacterial communities were at the carrying capacity of the high (+) carrying capacity systems and below in the low (−) systems. To gain a deeper understanding of the bacterial community characteristics the 16S rRNA gene of the bacterial community was sequenced at 1 and 9 DPH.Initial rearing condition did not leave a legacy effect on bacterial α-diversityThe bacterial α-diversity of the rearing water was investigated at 1 and 12 DPH (Fig. 2). At 1 DPH, the richness was comparable between the FTS−, FTS+ and MMS+ treatments, but on average, 1.5× higher for the MMS− treatment (307 vs 205 ASVs, Tukey’s test p  More

G.M., L.O.A., L.V.G. and L.E.O.C.A. thank the São Paulo Research Foundation (FAPESP) for funding (grants 2019/25701-8, 2020/08916-8, 2016/02018-2 and 2020/15230-5, respectively). L.O.A. and L.E.O.C.A. thank the National Council for Scientific and Technological Development (CNPq) for funding (grants 314473/2020-3 and 314416/2020-0, respectively). G.d.O. thanks the University of South Alabama Faculty Development Council Grant for funding (grant 279600-2022). More

Description of the study areaThe study covers urban, peri-urban and rural wetlands in the Bamenda Municipality of the North West Region of Cameroon that have evolved concomitantly with different stages of urbanization (Fig. 1). In this study, urbanisation is considered a loose term that is aimed at giving a geographical expression to the variation in the economic, social and cultural practices in the milieu. The central town with many economic activities is termed the urban, the fringe area with sprawls is termed peri-urban while the rural has typical peasant activities and make-shift structures. From the variation of human activities in the three sub-zones, a variety of chemical substances are discharged into drains, playing a substantial role in soil quality, and therefore plant macrophyte diversity. The Plants studied were ubiquitous in the area and verification of their IUCN conservation status in the red data book of plants of Cameroon confirmed their abundance14. Information on protected sites in Cameroon does not place the study area under conservation status. In line with that, permits are not required to undertake academic and research studies as well as do a responsible collection of plants in the study area. The urbanization rate of Bamenda is 42%, and the population grew from 48,111 inhabitants in 1976 to 488,883 inhabitants in 201015, with 150–200 inhabitants/km2.Figure 1Relief Map of Bamenda showing the Bamenda escarpment, topography and the location for quadrat sites.Full size imageThe study area is part of the Bamenda escarpment that is located between latitudes 5° 55″ N and 6° 30″ N and longitudes 10° 25″ E and 10° 67″ E. The town shows an altitudinal range of 1200–1700 m and is divided into two parts by escarpments—a low-lying and gently undulating part with altitudes ranging from 1200 to 1400 m, with many flat areas that are usually inundated for most parts of the year, and an elevated part that range from 1400 to 1700 m altitude. Most of the streams take their rise from this elevated part (Fig. 1).This area experiences two seasons—a rainy season (mid-March to mid-October) and a short dry season (mid-October to mid-March). The thermic and hyperthermic temperature regimes dominate in the area. The mean annual temperature stands at 19.9 °C. January and February are the hottest months with mean monthly temperatures of 29.1 and 29.7 °C, respectively. This area is dominated by the Ustic and Udic moisture regimes with the Udic extending to the south9. Annual rainfall ranges from 1300 to 3000 mm16. The area has a rich hydrographical network with intense human activities and a dense population along different water courses in the watershed. The area is bounded on the West, North and East by the Cameroon Volcanic Line (made up of basalts, trachytes, rhyolites and numerous salt springs). The geologic history of this area originates from the Precambrian era when there was a vast formation of geosynclinal complexes, which became filled with clay-calcareous, and sandstone sediments9. These materials, crossed by intrusions of crystalline rocks, were folded in a generally NE-SW direction and underwent variable metamorphism9. The Rocks in the area are thus of igneous (granitic and volcanic) and metamorphic (migmatites) origin17, which gives rise to ferralitic soils18.Agriculture is the principal human activity in and around this region18. The area equally harbours the commercial center that has factories ranging from soap production, and mechanic workshops to metallurgy, which may be potential sources of pollutants that can influence wetland Geochemistry. Raffia farinifera bush, which is largely limited to the wetlands, is an important vegetation type in this area. R. farinifera provides raffia wine, a vital economic resource to the inhabitants who are fighting against the cultivation of these wetlands by vegetable farmers.Methods of the studyMacrophyte diversity studyThe plant diversity of the wetlands was evaluated using quadrats in the dry season for accessibility reasons. For each of the three wetlands (the urban, peri-urban and rural areas), three transects were established on which representative quadrats, each measuring 10 m × 10 m, were mapped out in uncultivated areas for the determination of plant species cover-abundance and diversity. It is perceived that the different zones receive different mixtures of chemical substances and thus influence macrophyte diversity differently.According to a publication by14 on the vascular plants of Cameroon and a taxonomic checklist with IUCN assessment, the plants of the area are placed under the Least Concern Category(LC), and therefore not in the risky category. Diversity studies involved the identification of a specific area called “relevé” by progressively increasing test quadrat size and sampling for specific diversity until the smallest area with adequate species representation was reached. The relevé size determined here was 1 m2, making a total of 300 sub-quadrats (relevé) in the entire study ie. 100 in each main quadrat). For each site (main quadrat), 10 representative relevés were sampled and all plant species were enumerated. Most plant species in each of them were identified in the field by the Botanist, Dr Ndam Lawrence Monah using visual observation of the morphology of the leaves and flowers, a self-made field guide, the Flora of West Africa and the Flora of Cameroon. 10 unidentified plants were appropriately collected where there were in abundance, placed onto a conventional plant press and dried in the field. Voucher specimens were tagged and transported to the Limbe Botanic Gardens (SCA: Southern Cameroon, the code of the Limbe Botanic Gardens Herbarium) for identification. Mr Elias Ndive, the Taxonomist of the Limbe Botanic Gardens compared unidentified specimens with authentic herbarium specimens and other taxonomic guides and finally identified them. Voucher specimens of the 10 plants were given identification numbers and deposited in the Herbarium of the Limbe Botanic Gardens.The Braun–Banquet method was used19 for the assessment of species cover abundance. Relative abundance and abundance ratings were determined using the Braun–Banquet rating scheme (Table 1).Table 1 Braun-Blanquet rating scheme for vegetation cover-abundance, Source19.Full size tableSimpson’s diversity indexSpecies richness was evaluated using Simpson’s diversity index (D), which takes into account both species richness and the Braun-Blanquet rating scheme for vegetation cover abundance and evenness of abundance among the species present. In essence, D measures the probability that two individuals that are randomly selected from an area will belong to the same species. The formula for calculating D is presented as:$${text{D}} = frac{{sum {{text{n}}_{i} left( {{text{n}}_{i} – 1} right)} }}{{{text{N}}({text{N}} – 1)}}$$where ni = the total number of each species; N = the total number of individuals of all species.The value of D ranges from 0 to 1. With this index, 0 represents infinite diversity and 1 represents no diversity. That is, the larger the value the lower the diversity.Alternatively, Simpson’s Diversity Index, = 1–D,1-D was used as a measure of diversity because it is more logical and less likely to cause confusion. The scale then gives a score from 0 to 1 with higher scores showing higher diversity (instead of being associated with low scores).The Simpson index is a dominance index because it gives more weight to common or dominant species. In this case, a few rare species with only a few representatives will not affect the diversity.
Soil sampling and analysisSoil sampling was done in and around the three quadrats laid in the urban, peri-urban and rural wetlands for macrophytes sampling. Twenty-one (21) composite samples (0–25 cm) were randomly collected (Fig. 2) and taken to the laboratory in black plastic bags. Each composite sample was a collection of 5 dried core soil samples. Due to the observed greater heterogeneity in the urban sector, the sampling density was intensified. The soil samples were air-dried and screened through a 2-mm sieve. They were analyzed in duplicate for their physicochemical properties in the Environmental and Analytical Chemistry Laboratory of the University of Dschang, Cameroon. Particle size distribution, cation exchange capacity (CEC), exchangeable bases, electrical conductivity (EC) and pH were determined by standard procedures20. Soil pH was measured both in water and KCl (1:2.5 soil/water mixture) using a glass electrode pH meter. Part of the soil was ball-milled for organic carbon (Walkley–Black method) and total nitrogen (Macro-Kjeldahl method) as largely described by20. Available phosphorus (P) was determined by Bray I method. Exchangeable cations were extracted using 1 N ammonium acetate at pH 7. Potassium (K) and sodium (Na) in the extract were determined using a flame photometer and magnesium (Mg) and calcium (Ca) were determined by complexiometric titration. Exchange acidity was extracted with 1 M KCl followed by quantification of Al and H by titration20. Effective cation exchange capacity (ECEC) was determined as the sum of bases and exchanged acidity.Figure 2Adapted from the 1980 land use map of the Bamenda City Area showing soil sampling points: Source Bamenda City Council.Map of the study area in freshwater wetlands of Bamenda Municipality.Full size imageApparent CEC (CEC at pH 7) was determined directly as outlined by20. Based on critical values of nutrients established for vegetables, nutrients were declared sufficient or deficient.
Statistical analysisThe data were subjected to statistical analysis using Microsoft Excel 2007 and SPSS statistical package 20.0. Soil properties were assessed for their variability using the coefficient of variation (CV) and compared with variability classes (Table 2).$$CV=frac{Sd}{X}X 100$$where: Sd = standard deviation; = X arithmetic mean of soil properties.Table 2 Grouping coefficient of variation into variability classes.Full size tableThe hierarchical cluster analysis (HCA) was used to group the area under managing units. The main goal of the hierarchical agglomerative cluster analysis is to spontaneously classify the data into groups of similarity (clusters). This is done by searching objects in the n-dimensional space that is located in the closest neighborhood and separating a stable cluster from other clusters. The sampling sites were considered objects for classification. Each object was determined by a set of variables (chemical concentrations of the soils in this case). More

ResultsSoil physiochemical propertiesThe preliminary status of the soil analyzed before the commencement of the field preparatory activities revealed that the soil was subtlety fertile with regard to the physical and chemical properties (Table 1).Table 1 Physicochemical properties of the soil.Full size tableAssessment of disease incidence at sowing dates during each year in the trial studyIn the trial study, very low and statistically significant (p  More

Study population and study designThis is a prospective interventional cohort study of healthy S. aureus carriers and noncarriers in the Netherlands. All experiments were performed in accordance with the Dutch Medical Research Involving Human Subjects Act (WMO). The study protocol was approved by the local Medical Ethical Committee of the Erasmus University Medical Centre Rotterdam, The Netherlands (MEC-2018-091). Written informed consent was obtained for all participants. Participants were recruited through advertisements at Dutch universities and the research teams social networks. Exclusion criteria were age  8 CFU/mL for each culture. Noncarriers were defined as 2 S. aureus-negative cultures. Intermittent S. aureus carriers were excluded from further participation in the study. Eligible volunteers were enrolled on a first-come, first-served basis.Eligible participants were asked to fill out a questionnaire regarding risk factors for S. aureus acquisition. All participants received decolonization treatment. Decolonization consisted of mupirocin nasal ointment (2%, GlaxoSmithKline BV, Zeist, the Netherlands) twice daily and chlorhexidine gluconate cutaneous solution (4%w/v, Regent Medical Overseas Limited, Oldham, UK) once daily, both for 5 days.Nasal samples were taken 1 day before decolonization (D0) and 2 days (D7), 1 month (M1), 3 months (M3) and 6 months (M6) after decolonization. All participants received a personal demonstration for nasal sampling by the executive researcher. Thereafter, all specimens were taken by the participants by inserting a swab (ESwab, 490CE.A, Copan Italia, Brescia, Italy) into one nostril and rotating 5 times, repeating this in the second nostril using the same swab. Swabs were collected in a container filled with 1 ml modified Liquid Amies, a collection and transport solution, and sent through regular mail service (non-temperature controlled) or deposited at the laboratory personally.
Staphylococcus aureus quantitative cultureQuantitative S. aureus cultures were conducted to examine the dynamics of S. aureus carriage over the 6-month follow-up period after decolonization. Swab containers were vortexed for 20 s before plating. Serial dilutions of Amies medium were plated onto phenol mannitol salt agar (PHMA) and incubated for 2 days at 37 °C. Swabs were placed in phenol mannitol salt broth (PHMB) and incubated for 7 days at 37 °C for enrichment. S. aureus growth was confirmed by a latex agglutination test (Staph Plus Latex Kit, Diamondial, Vienna, Austria). Morphologically different S. aureus colonies were selected for spa typing and methicillin resistance screening using BBL CHROMagar MRSA II agar (BD, Breda, The Netherlands).
Spa typingMolecular typing of S. aureus isolates was performed to infer whether recolonization with S. aureus in decolonized carriers involved the same spa-type. Typing was limited to the last S. aureus positive culture moment and the last S. aureus positive culture moment after decolonization in recolonised carriers. S. aureus DNA lysates were prepared by boiling in 10 mM Tris–HCl, 1 mM disodium EDTA, pH 8.0 or extraction with the QIAamp DNA Mini Kit (QIAGEN, Venlo, The Netherlands) according to the manufacturer’s instructions. Amplification of the S. aureus protein A (spa) repeat region was performed by PCR with 2 sets of primers. One set consisted of forward primer spa-1113, 5′-TAAAGACGATCCTTCGGTGAGC-3′ and reverse primer spa-1514, 5′-CAGCAGTAGTGCCGTTTGCTT-3′24. The other set consisted of forward primers spa-F1, 5′-AACAACGTAACGGCTTCATCC-3′ and spa-F2 5′-AGACGATCCTTCAGTGAGC-3′ and reverse primer spa-R1 5′-GCTTTTGCAATGTCATTTACTG-3′. Amplicons were purified with ExoSAP-IT (Applied Biosystems) according to the manufacturer’s instructions and sent for sequence analysis (Baseclear, Leiden, The Netherlands). Resulting sequences were analysed using BioNumerics v7.6 (Applied Maths NV, Sint-Martens-Latem, Belgium) and the spa types were assigned by use of the RidomStaphType database (Ridom GmbH, Würzburg, Germany).16S ribosomal RNA sequencing of nasal microbiotaThe impact of decolonization on the nasal microbiome and the recovery of the microbiome structure after decolonization were examined by means of 16S rRNA metabarcoding. Amies medium from each nasal swab container was stored at − 80 °C on the day of receipt at the study laboratory in Rotterdam, NL, then sent at − 80 °C to the microbiome analysis laboratory in Lyon, FR. To properly capture the impact of decolonization on the living microbiota, metabarcoding used RNA-based 16S ribosomal RNA (rRNA, which is preserved in living cells but quickly cleared after cell death or lysis) rather than the DNA coding sequence, as DNA can persist for prolonged time periods after cell death25,26,27,28. RNA was extracted using the Mag Bind® Total RNA 96 Kit (Omega Bio-tek) tissue protocol from 150 µL of samples’ material. Cell lysis was performed using beads (Disruptor plate C plus—Omega Bio-tek) and proteinase K for 15 min at 2600 rpm, followed by 10 min at room temperature without agitation, and finished with a DNase I digestion of 20 min at room temperature. RNA was quantified using QuantiFluor RNA kit on Tecan Safire (TECAN). 10 ng total RNA was used for reverse transcription using FIREScript RT cDNA synthesis kit (Solis Biodyne) with random primers, then cDNA was purified with SPRIselect reagent (Beckman coulter) and quantified.The rRNA V1–V3 region was PCR amplified using the 5× HOT BIOAmp® BlendMaster Mix 12,5 mM MgCl 2 (Biofidal), 10× GC rich Enhancer (Biofidal) and BSA 20 mg/mL. The PCR reaction consisted of 30 cycles at 56 °C using the forward primer 27F, 5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG AGAGTTTGATCCTGGCTCAG-3′ and reverse primer 534R, 5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGATTACCGCGGCTGCTGG-3′ in 25 µL of solution. PCR products were purified using SPRIselect beads (Beckman Coulter) in 20 µL nuclease-free water and quantified using QuantiFluor dsDNA (Promega). Samples were indexed with lllumina’s barcodes with the same PCR reagents during a 12 cycles PCR, then purified and quantified as previously mentioned. Samples were normalized and pooled, then sequenced using Illumina MiSeq V3 Flow Cell following the constructor’s recommendations for a 2 × 300 bp paired-end application. A mean of 130 k proofread reads per sample was obtained.Experiment buffers were used as negative controls to detect contamination by out-of-sample bacterial RNA. RNA extraction was controlled using an in-house mix of live Staphylococcus aureus ATCC29213 and Escherichia coli ATCC25922 in equal proportions, allowing for assessing extraction bias in Gram-positive and -negative bacteria. PCR amplification bias was controlled using a commercial DNA mix of 8 bacterial species (ZymoBIOMICS™ Microbial Community DNA Standard).Bioinformatics and statistical analysesSequencing reads were quality checked and trimmed. Paired-ended read pairs were merged using BBMap version 38.49 (available at https://sourceforge.net/projects/bbmap/), with default options besides a minimum single size of 150 bp with an average Phred quality score higher than 10, and a total pair size of minimum 400 bp. PCR adapters were removed with cutadapt v.2.1 (Martin 2011) then dereplicated using vsearch v.2.12.029 with the sizeout option. For species assignment, reads were aligned to sequences of NCBI blast 16S_ribosomal_RNA database (version date 03.12.2020) using Blastn v.2.11.0+30,31, keeping a maximum of 20 reference targets. Read counts per bacterial species were normalized to account for taxon-specific variations of the copy number of 16S rRNA genes using NCBI rrnDB-5.5 database based on the mean gene copy number in the taxon.To optimize the resolution of sequencing read taxonomic assignment, we used in-house bioinformatic software publicly available at https://github.com/rasigadelab/taxonresolve. Briefly, when a read matches sequences from several species with identical alignment scores, taxonomic assignment pipelines typically output the higher taxonomic level such as the genus (e.g., Staphylococcus spp. when a read matches S. aureus and S. epidermidis). This loss of information can be problematic when species-level discrimination is important. To prevent losing species-level information, the taxonresolve software assigns reads with uncertain species to groups of species rather than to genera.Bacterial species deemed present from contaminating sources such as kits reagents and found in negative controls, mostly from the Bacillus genera, were removed. A total of 1376 species or group of species were retained. The rarefaction curves corresponding to the sequencing effort to assess the species richness within samples are shown in Supplementary Fig. 3. Most samples reached a plateau after 40,000 sequences.Given the small sample size compared to the number of variables and species considered in this study, no hypothesis testing was performed, and we provide a descriptive assessment of the results. In figures, 95% confidence intervals of the means were computed based on normal approximation, after log transformation for CFU/mL and log odds transformation for quantities restricted to the [0, 1] interval, such as proportions.In microbial diversity analyses, we retained the 9 most prevalent bacterial species and pooled the other species into an ‘Others’ category. To assess the disruption and possible recovery of the microbiota, the divergence of sampled microbiota relative to the initial, pre-treatment microbiota (D0) was assessed using the Bray–Curtis dissimilarity at each sampling time point relative to the first sample of the same patient.Software code of the analyses are available at https://github.com/rasigadelab/macotra-metabarcoding. Data are available at https://zenodo.org/record/6382657. Analyses and figures used R software v3.6.032 with packages dplyr33, ggplot234, vegan35, and MicrobiomAnalyst available at https://www.microbiomeanalyst.ca36,37. More

Tsai, C.-W., Lai, C.-F., Chao, H.-C. & Vasilakos, A. V. Big data analytics: a survey. J. Big Data 2, 21 (2015).
Google Scholar 
Lemoine, F. et al. Renewing Felsenstein’s phylogenetic bootstrap in the era of big data. Nature 556, 452–456 (2018).ADS 
CAS 

Google Scholar 
Manzoni, C. et al. Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences. Brief. Bioinform. 19, 286–302 (2018).CAS 

Google Scholar 
Lichtman, J. W., Pfister, H. & Shavit, N. The big data challenges of connectomics. Nat. Neurosci. 17, 1448–1454 (2014).CAS 

Google Scholar 
Altaf-Ul-Amin, M., Afendi, F. M., Kiboi, S. K. & Kanaya, S. Systems biology in the context of big data and networks. Biomed. Res. Int. 2014, 428570 (2014).
Google Scholar 
Xia, J., Wang, J. & Niu, S. Research challenges and opportunities for using big data in global change biology. Glob. Change Biol. 26, 6040–6061 (2020).ADS 

Google Scholar 
Hindell, M. A. et al. Tracking of marine predators to protect Southern Ocean ecosystems. Nature 580, 87–92 (2020).ADS 
CAS 

Google Scholar 
Hussey, N. E. et al. Ecology. Aquatic animal telemetry: A panoramic window into the underwater world. Science 348, 1255642 (2015).
Google Scholar 
Kays, R., Crofoot, M. C., Jetz, W. & Wikelski, M. Terrestrial animal tracking as an eye on life and planet. Science 348, aaa2478 (2015).
Google Scholar 
Sherub, S., Fiedler, W., Duriez, O. & Wikelski, M. Bio-logging, new technologies to study conservation physiology on the move: a case study on annual survival of Himalayan vultures. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 203, 531–542 (2017).
Google Scholar 
Nathan, R. et al. Big-data approaches lead to an increased understanding of the ecology of animal movement. Science 375, eabg1780 (2022).CAS 

Google Scholar 
Wilson, R. P. et al. Estimates for energy expenditure in free-living animals using acceleration proxies: A reappraisal. J. Anim. Ecol. 89, 161–172 (2020).
Google Scholar 
Patterson, A., Gilchrist, H. G., Chivers, L., Hatch, S. & Elliott, K. A comparison of techniques for classifying behavior from accelerometers for two species of seabird. Ecol. Evol. 9, 3030–3045 (2019).
Google Scholar 
Masello, J. F. et al. How animals distribute themselves in space: energy landscapes of Antarctic avian predators. Mov. Ecol. 9, 24 (2021).
Google Scholar 
Shepard, E. L. C. et al. Energy landscapes shape animal movement ecology. Am. Nat. 182, 298–312 (2013).
Google Scholar 
Elliott, K. H., Le Vaillant, M., Kato, A., Speakman, J. R. & Ropert-Coudert, Y. Accelerometry predicts daily energy expenditure in a bird with high activity levels. Biol. Lett. 9, 20120919 (2013).
Google Scholar 
Nickel, B. A., Suraci, J. P., Nisi, A. C. & Wilmers, C. C. Energetics and fear of humans constrain the spatial ecology of pumas. Proc. Natl. Acad. Sci. USA 118, e2004592118 (2021).
Eisaguirre, J. M., Booms, T. L., Barger, C. P., Lewis, S. B. & Breed, G. A. Novel step selection analyses on energy landscapes reveal how linear features alter migrations of soaring birds. J. Anim. Ecol. 89, 2567–2583 (2020).
Google Scholar 
Wittemyer, G., Northrup, J. M. & Bastille-Rousseau, G. Behavioural valuation of landscapes using movement data. Philos. Trans. R. Soc. Lond. B Biol. Sci. 374, 20180046 (2019).
Google Scholar 
Chimienti, M. et al. The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data. Ecol. Evol. 6, 727–741 (2016).
Google Scholar 
Hounslow, J. L. et al. Assessing the effects of sampling frequency on behavioural classification of accelerometer data. J. Exp. Mar. Bio. Ecol. 512, 22–30 (2019).
Google Scholar 
Glass, T. W., Breed, G. A., Robards, M. D., Williams, C. T. & Kielland, K. Accounting for unknown behaviors of free-living animals in accelerometer-based classification models: Demonstration on a wide-ranging mesopredator. Ecol. Inf. 60, 101152 (2020).
Google Scholar 
Wang, Y. et al. Movement, resting, and attack behaviors of wild pumas are revealed by tri-axial accelerometer measurements. Mov. Ecol. 3, 2 (2015).
Google Scholar 
Chakravarty, P., Cozzi, G., Ozgul, A. & Aminian, K. A novel biomechanical approach for animal behaviour recognition using accelerometers. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.13172 (2019).Article 

Google Scholar 
Clarke, T. M. et al. Using tri-axial accelerometer loggers to identify spawning behaviours of large pelagic fish. Mov. Ecol. 9, 26 (2021).
Google Scholar 
Zhang, J., O’Reilly, K. M., Perry, G. L. W., Taylor, G. A. & Dennis, T. E. Extending the functionality of behavioural change-point analysis with k-Means clustering: a case study with the little penguin (Eudyptula minor). PLoS ONE 10, e0122811 (2015).
Google Scholar 
Korpela, J. et al. Machine learning enables improved runtime and precision for bio-loggers on seabirds. Commun. Biol. 3, 633 (2020).
Google Scholar 
Jeantet, L. et al. Behavioural inference from signal processing using animal-borne multi-sensor loggers: a novel solution to extend the knowledge of sea turtle ecology. R. Soc. Open Sci. 7, 200139 (2020).ADS 

Google Scholar 
Wang, G. Machine learning for inferring animal behavior from location and movement data. Ecol. Inf. 49, 69–76 (2019).
Google Scholar 
Dunford, C. E. et al. Surviving in steep terrain: a lab-to-field assessment of locomotor costs for wild mountain lions (Puma concolor). Mov. Ecol. 8, 34 (2020).
Google Scholar 
Jeanniard-du-Dot, T., Guinet, C., Arnould, J. P. Y., Speakman, J. R. & Trites, A. W. Accelerometers can measure total and activity-specific energy expenditures in free-ranging marine mammals only if linked to time-activity budgets. Funct. Ecol. 31, 377–386 (2017).
Google Scholar 
Hicks, O. et al. Acceleration predicts energy expenditure in a fat, flightless, diving bird. Sci. Rep. 10, 21493 (2020).ADS 
CAS 

Google Scholar 
Dentinger, J. E. et al. A probabilistic framework for behavioral identification from animal-borne accelerometers. Ecol. Model. 464, 109818 (2022).
Google Scholar 
Chakravarty, P., Maalberg, M., Cozzi, G., Ozgul, A. & Aminian, K. Behavioural compass: animal behaviour recognition using magnetometers. Mov. Ecol. 7, 28 (2019).
Google Scholar 
Hammond, T. T., Palme, R. & Lacey, E. A. Ecological specialization, variability in activity patterns and response to environmental change. Biol. Lett. 14, 20180115 (2018).
Google Scholar 
Lynch, H. J. & LaRue, M. A. First global census of the Adélie Penguin. Auk 131, 457–466 (2014).
Google Scholar 
Riaz, J., Bestley, S., Wotherspoon, S., Freyer, J. & Emmerson, L. From trips to bouts to dives: temporal patterns in the diving behaviour of chick-rearing Adélie penguins East Antarctica. Mar. Ecol. Prog. Ser. 654, 177–194 (2020).ADS 

Google Scholar 
Cherel, Y. Isotopic niches of emperor and Adélie penguins in Adélie Land, Antarctica. Mar. Biol. 154, 813–821 (2008).
Google Scholar 
Little Penguin (Eudyptula minor) – BirdLife species factsheet. at Carroll, G., Harcourt, R., Pitcher, B. J., Slip, D. & Jonsen, I. Recent prey capture experience and dynamic habitat quality mediate short-term foraging site fidelity in a seabird. Proc. Biol. Sci. 285, 20180788 (2018).
Google Scholar 
Meyer, X. et al. Oceanic thermal structure mediates dive sequences in a foraging seabird. Ecol. Evol. 10, 6610–6622 (2020).
Google Scholar 
Cavallo, C. et al. Quantifying prey availability using the foraging plasticity of a marine predator, the little penguin. Funct. Ecol. https://doi.org/10.1111/1365-2435.13605 (2020).Article 

Google Scholar 
Ropert-Coudert, Y., Chiaradia, A. & Kato, A. An exceptionally deep dive by a Little Penguin Eudyptula minor. Mar. Ornithol 34, 71–74 (2006).
Google Scholar 
Ropert-Coudert, Y., Kato, A., Wilson, R. P. & Cannell, B. Foraging strategies and prey encounter rate of free-ranging Little Penguins. Mar. Biol. 149, 139–148 (2006).
Google Scholar 
Rodríguez, A., Chiaradia, A., Wasiak, P., Renwick, L. & Dann, P. Waddling on the dark side: ambient light affects attendance behavior of little penguins. J. Biol. Rhythms 31, 194–204 (2016).
Google Scholar 
Ropert-Coudert, Y. et al. Happy feet in a hostile world? the future of penguins depends on proactive management of current and expected threats. Front. Mar. Sci. 6, 248 (2019).
Google Scholar 
Shuert, C. R., Pomeroy, P. P. & Twiss, S. D. Assessing the utility and limitations of accelerometers and machine learning approaches in classifying behaviour during lactation in a phocid seal. Anim. Biotelemetry 6, 14 (2018).
Google Scholar 
Dickinson, E. R. et al. Limitations of using surrogates for behaviour classification of accelerometer data: refining methods using random forest models in Caprids. Mov. Ecol. 9, 28 (2021).
Google Scholar 
Conway, A. M., Durbach, I. N., McInnes, A. & Harris, R. N. Frame-by-frame annotation of video recordings using deep neural networks. Ecosphere 12, e03384 (2021).
Google Scholar 
Ravindran, S. Five ways deep learning has transformed image analysis. Nature 609, 864–866 (2022).ADS 
CAS 

Google Scholar 
Del Caño, M. et al. Fine-scale body and head movements allow to determine prey capture events in the Magellanic Penguin (Spheniscus magellanicus). Mar. Biol. 168, 84 (2021).
Google Scholar 
Johnson, J. M. & Khoshgoftaar, T. M. Survey on deep learning with class imbalance. J. Big Data 6, 27 (2019).
Google Scholar 
Hazen, E. L. et al. Marine top predators as climate and ecosystem sentinels. Front. Ecol. Environ. 17, 565–574 (2019).
Google Scholar 
Sánchez, S. et al. Within-colony spatial segregation leads to foraging behaviour variation in a seabird. Mar. Ecol. Prog. Ser. 606, 215–230 (2018).ADS 

Google Scholar 
Patrick, S. C., Martin, J. G. A., Ummenhofer, C. C., Corbeau, A. & Weimerskirch, H. Albatrosses respond adaptively to climate variability by changing variance in a foraging trait. Glob. Change Biol. https://doi.org/10.1111/gcb.15735 (2021).Article 

Google Scholar 
Bonar, M. et al. Geometry of the ideal free distribution: individual behavioural variation and annual reproductive success in aggregations of a social ungulate. Ecol. Lett. 23, 1360–1369 (2020).
Google Scholar 
Michelot, C., Kato, A., Raclot, T. & Ropert-Coudert, Y. Adélie penguins foraging consistency and site fidelity are conditioned by breeding status and environmental conditions. PLoS ONE 16, e0244298 (2021).CAS 

Google Scholar 
Mahoney, P. J. et al. Navigating snowscapes: scale-dependent responses of mountain sheep to snowpack properties. Ecol. Appl. 28, 1715–1729 (2018).
Google Scholar 
Watanabe, Y. Y., Ito, K., Kokubun, N. & Takahashi, A. Foraging behavior links sea ice to breeding success in Antarctic penguins. Sci. Adv. 6, eaba4828 (2020).ADS 

Google Scholar 
Lescroël, A. et al. Working less to gain more: when breeding quality relates to foraging efficiency. Ecology 91, 2044–2055 (2010).
Google Scholar 
Zimmer, I., Ropert-Coudert, Y., Kato, A., Ancel, A. & Chiaradia, A. Does foraging performance change with age in female little penguins (Eudyptula minor)?. PLoS ONE 6, e16098 (2011).ADS 
CAS 

Google Scholar 
Hertel, A. G., Royauté, R., Zedrosser, A. & Mueller, T. Biologging reveals individual variation in behavioural predictability in the wild. J. Anim. Ecol. 90, 723–737 (2021).
Google Scholar 
Dickinson, E. R., Stephens, P. A., Marks, N. J., Wilson, R. P. & Scantlebury, D. M. Best practice for collar deployment of tri-axial accelerometers on a terrestrial quadruped to provide accurate measurement of body acceleration. Anim. Biotelemetry 8, 9 (2020).
Google Scholar 
Garde, B. et al. Ecological inference using data from accelerometers needs careful protocols. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.13804 (2022).Article 

Google Scholar 
Watanabe, Y. Y., Ito, M. & Takahashi, A. Testing optimal foraging theory in a penguin-krill system. Proc. Biol. Sci. 281, 20132376 (2014).
Google Scholar 
Grémillet, D. et al. Energetic fitness: Field metabolic rates assessed via 3D accelerometry complement conventional fitness metrics. Funct. Ecol. 32, 1203–1213 (2018).
Google Scholar 
Chimienti, M. et al. Quantifying behavior and life-history events of an Arctic ungulate from year-long continuous accelerometer data. Ecosphere 12, e03565 (2021).
Google Scholar 
Sutton, G. J., Botha, J. A., Speakman, J. R. & Arnould, J. P. Y. Validating accelerometry-derived proxies of energy expenditure using the doubly-labelled water method in the smallest penguin species. Biol. Open 10, bio055475 (2021).
Google Scholar 
Pagano, A. M. & Williams, T. M. Estimating the energy expenditure of free-ranging polar bears using tri-axial accelerometers: A validation with doubly labeled water. Ecol. Evol. 9, 4210–4219 (2019).
Google Scholar 
Ballance, L. T., Ainley, D. G., Ballard, G. & Barton, K. An energetic correlate between colony size and foraging effort in seabirds, an example of the Adélie penguin Pygoscelis adeliae. J. Avian Biol. 40, 279–288 (2009).
Google Scholar 
Wilson, R. P. et al. Long-term attachment of transmitting and recording devices to penguins and other seabirds. Wildl. Soc. Bull. 25, 101–106 (1997).
Google Scholar 
Shepard, E. L. C. et al. Identification of animal movement patterns using tri-axial accelerometry. Endanger. Species Res. 10, 47–60 (2008).ADS 

Google Scholar 
Kato, A., Ropert-Coudert, Y., Grémillet, D. & Cannell, B. Locomotion and foraging strategy in foot-propelled and wing-propelled shallow-diving seabirds. Mar. Ecol. Prog. Ser. 308, 293–301 (2006).ADS 

Google Scholar 
Team, R. C. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://url.org/www.R-project.org/ (2021).
Ainley, D. The Adélie Penguin: Bellwether of Climate Change (New York: Columbia University Press) (2006).Langrognet, F. et al. Rmixmod: Classification with Mixture Modelling. (2020).Bishop, C. M. Pattern Recognition and Machine Learning. Springer Science+Business Media, LLC, New
York, NY. (2006).Amélineau, F. et al. Intra- and inter-individual changes in little penguin diving and isotopic composition over the breeding season. Mar. Biol. 168, 62 (2021).
Google Scholar 
Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecol. Evol. 8, 1639–1644 (2017).
Google Scholar 
Kuhn, M. & Wickham, H. Tidymodels: a collection of packages for modeling and machine learning using tidyverse principles. https://www.tidymodels.org (2020).Wright, M. N. & Ziegler, A. Ranger : A fast implementation of random forests for high dimensional data in C++ andR. J. Stat. Softw. 77, 1–17 (2017).
Google Scholar  More