Ecology

Chen, I. C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333(6045), 1024–1026. https://doi.org/10.1126/SCIENCE.1206432 (2011).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Newton, I. Migration within the annual cycle: Species, sex and age differences. J. Ornithol. 152, 169–185. https://doi.org/10.1007/S10336-011-0689-Y/TABLES/1 (2011).Article 

Google Scholar 
Dodson, S., Abrahms, B., Bograd, S. J., Fiechter, J. & Hazen, E. L. Disentangling the biotic and abiotic drivers of emergent migratory behavior using individual-based models. Ecol. Model. 432, 109225. https://doi.org/10.1016/J.ECOLMODEL.2020.109225 (2020).Article 

Google Scholar 
Lehikoinen, A. et al. Sex-specific timing of autumn migration in birds: the role of sexual size dimorphism, migration distance and differences in breeding investment. Ornis Fennica 94, 53–65 (2017).
Google Scholar 
Stewart, B. S. Ontogeny of differential migration and sexual segregation in northern elephant seals. J. Mammol. 78(4), 1101–1116 (1997).Somveille, M., Rodrigues, A. S. L. & Manica, A. Why do birds migrate? A macroecological perspective. Glob. Ecol. Biogeogr. 24(6), 664–674. https://doi.org/10.1111/geb.12298 (2015).Article 

Google Scholar 
Corkeron, P. J. & Connor, R. C. Why do baleen whales migrate?. Mar. Mamm. Sci. 15(4), 1228–1245. https://doi.org/10.1111/J.1748-7692.1999.TB00887.X (1999).Article 

Google Scholar 
Mourier, J., Mills, S. C. & Planes, S. Population structure, spatial distribution and life-history traits of blacktip reef sharks Carcharhinus melanopterus. J. Fish Biol. 82(3), 979–993. https://doi.org/10.1111/JFB.12039 (2013).Article 
CAS 
PubMed 

Google Scholar 
Avgar, T., Mosser, A., Brown, G. S. & Fryxell, J. M. Environmental and individual drivers of animal movement patterns across a wide geographical gradient. J. Anim. Ecol. 82, 96–106. https://doi.org/10.1111/j.1365-2656.2012.02035.x (2013).Article 
PubMed 

Google Scholar 
Crawshaw, L. I. Physiological and behavioral reactions of fishes to temperature change. J. Fish. Res. Board Can. 34(5), 730–734. https://doi.org/10.1139/f77-113 (1977).Article 

Google Scholar 
Heithaus, M., Dill, L., Marshall, G. J. & Buhleier, B. Habitat use and foraging behavior of tiger sharks (Galeocerdo cuvier) in a seagrass ecosystem. Mar. Biol. 140, 337–348. https://doi.org/10.1007/s00227-001-0711-7 (2002).Article 

Google Scholar 
Magnuson, J. J., Crowder, L. B. & Medvick, P. A. Temperature as an ecological resource. Integr. Comp. Biol. 19(1), 331–343. https://doi.org/10.1093/icb/19.1.331 (1979).Article 

Google Scholar 
Matern, S. A., Cech, J. J. & Hopkins, T. E. Diel movements of bat rays, Myliobatis californica, in Tomales Bay, California: Evidence for behavioral thermoregulation?. Environ. Biol. Fishes 58(2), 173–182. https://doi.org/10.1023/A:1007625212099 (2000).Article 

Google Scholar 
Speed, C. W., Meekan, M. G., Field, I. C., McMahon, C. R. & Bradshaw, C. J. A. Heat-seeking sharks: Support for behavioural thermoregulation in reef sharks. Mar. Ecol. Prog. Ser. 463, 231–244. https://doi.org/10.3354/meps09864 (2012).Article 
ADS 

Google Scholar 
Dewar, H., Domeier, M. & Nasby-Lucas, N. Insights into young of the year white shark, Carcharodon carcharias, behavior in the Southern California Bight. Environ. Biol. Fishes https://doi.org/10.1023/B:EBFI.0000029343.54027.6a.pdf (2004).Article 

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

Google Scholar 
Heupel, M. R., Simpfendorfer, C. A. & Hueter, R. E. Estimation of shark home ranges using passive monitoring techniques. Environ. Biol. Fishes 71(2), 135–142. https://doi.org/10.1023/b:ebfi.0000045710.18997.f7 (2004).Article 

Google Scholar 
Topping, D. T., Lowe, C. G. & Caselle, J. E. Site fidelity and seasonal movement patterns of adult California sheephead Semicossyphus pulcher (Labridae): An acoustic monitoring study. Mar. Ecol. Progr. Ser. 326, 257–267 (2006).Weng, K. C. et al. Movements, behavior and habitat preferences of juvenile white sharks Carcharodon carcharias in the eastern Pacific. Mar. Ecol. Prog. Ser. 338, 211–224. https://doi.org/10.3354/meps338211 (2007).Article 
ADS 

Google Scholar 
Lyons, K. et al. The degree and result of gillnet fishery interactions with juvenile white sharks in southern California assessed by fishery-independent and -dependent methods. Fish. Res. 147, 370–380. https://doi.org/10.1016/J.FISHRES.2013.07.009 (2013).Article 
ADS 

Google Scholar 
Papastamatiou, Y. P. et al. Drivers of daily routines in an ectothermic marine predator: Hunt warm, rest warmer?. PLoS ONE. https://doi.org/10.1371/journal.pone.0127807 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Adolph, S. C. Influence of behavioral thermoregulation on microhabitat use by two sceloporus lizards. Ecology 71(1), 315–327. https://doi.org/10.2307/1940271 (1990).Article 

Google Scholar 
Heithaus, M. R. The biology of tiger sharks, Galeocerdo cuvier, in Shark Bay, Western Australia: sex ratio, size distribution, diet, and seasonal changes in catch rates. Environ. Biol. Fishes 61, 25–36 (2001).Article 

Google Scholar 
Vaudo, J. J. & Lowe, C. G. Movement patterns of the round stingray Urobatis halleri(Cooper) near a thermal outfall. J. Fish Biol. 68(6), 1756–1766. https://doi.org/10.1111/j.0022-1112.2006.01054.x (2006).Article 

Google Scholar 
Vaudo, J. J. & Heithaus, M. R. Microhabitat selection by marine mesoconsumers in a thermally heterogeneous habitat: Behavioral thermoregulation or avoiding predation risk?. PLoS ONE. 8(4), e61907. https://doi.org/10.1371/journal.pone.0061907 (2013).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Weng, K. C. et al. Migration and habitat of white sharks (Carcharodon carcharias) in the eastern Pacific Ocean. Mar. Biol. 152(4), 877–894. https://doi.org/10.1007/s00227-007-0739-4 (2007).Article 

Google Scholar 
White, C. F. et al. Quantifying habitat selection and variability in habitat suitability for juvenile white sharks. PLoS ONE 14(5), e0214642. https://doi.org/10.1371/journal.pone.0214642 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Curtis, T. H. et al. First insights into the movements of young-of-the-year white sharks (Carcharodon carcharias) in the western North Atlantic Ocean. Sci. Rep. 8(1), 1–8. https://doi.org/10.1038/s41598-018-29180-5 (2018).Article 
ADS 
CAS 

Google Scholar 
Bruce, B. D., Harasti, D., Lee, K., Gallen, C. & Bradford, R. Broad-scale movements of juvenile white sharks Carcharodon carcharias in eastern Australia from acoustic and satellite telemetry. Mar. Ecol. Prog. Ser. 619, 1–15. https://doi.org/10.3354/MEPS12969 (2019).Article 
ADS 

Google Scholar 
Carey, F. G. et al. Temperature and activities of a white shark Carcharodon carcharias. Copeia 2, 254–260. https://doi.org/10.2307/1444603 (1982).Article 

Google Scholar 
Klimley, A. P., Beavers, S. C., Curtis, T. H. & Jorgensen, S. J. Movements and swimming behavior of three species of sharks in La Jolla Canyon, California. Environ. Biol. Fish. 63, 117–135. https://doi.org/10.1023/A:1014200301213.pdf (2002).Article 

Google Scholar 
Towner, A. V., Underhill, L. G., Jewell, O. J. D. & Smale, M. J. Environmental Influences on the abundance and sexual composition of white sharks Carcharodon carcharias in Gansbaai, South Africa. PLoS ONE. 8(8), e71197. https://doi.org/10.1371/journal.pone.0071197 (2013).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Anderson, J. M. et al. High-resolution acoustic telemetry reveals swim speeds and inferred field metabolic rates in juvenile white sharks (Carcharodon carcharias). PLoS ONE 17(6), e0268914. https://doi.org/10.1371/JOURNAL.PONE.0268914 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Anderson, J. M. et al. Interannual nearshore habitat use of young of the year white sharks off Southern California. Front. Mar. Sci. 8, 238. https://doi.org/10.3389/fmars.2021.645142 (2021).Article 

Google Scholar 
Domeier, M. L. & Nasby-Lucas, N. Two-year migration of adult female white sharks (Carcharodon carcharias) reveals widely separated nursery areas and conservation concerns. Anim. Biotelemet. 1(1), 1–10. https://doi.org/10.1186/2050-3385-1-2/FIGURES/3 (2013).Article 

Google Scholar 
Oñate-González, E. C. et al. Importance of Bahia Sebastian Vizcaino as a nursery area for white sharks (Carcharodon carcharias) in the Northeastern Pacific: A fishery dependent analysis. Fish. Res. 188, 125–137. https://doi.org/10.1016/J.FISHRES.2016.12.014 (2017).Article 

Google Scholar 
Lowe, C. G. et al. Historic fishery interactions with white sharks in the Southern California Bight. Glob. Perspect. Biol. Life Hist. White Shark 14, 169–190 (2012).
Google Scholar 
Anderson, J. M. et al. Non-random Co-occurrence of Juvenile White Sharks (Carcharodon carcharias) at Seasonal Aggregation Sites in Southern California. Front. Mar. Sci. 8, 1–14. https://doi.org/10.3389/fmars.2021.688505 (2021).Article 
ADS 
CAS 

Google Scholar 
Benson, J. F. et al. Juvenile survival, competing risks, and spatial variation in mortality risk of a marine apex predator. J. Appl. Ecol. 55, 2888–2897. https://doi.org/10.1111/1365-2664.13158 (2018).Article 

Google Scholar 
RStudio Team. RStudio: Integrated Development for R. (RStudio, PBC, 2020) http://www.rstudio.com/.Derrick, T., & Thomas, J. Time Series Analysis: The Cross-Correlation Function. Innovative Analyses of Human Movement, Chapter 7. https://lib.dr.iastate.edu/kin_pubs/46 (2004).Killick, R., Fearnhead, P. & Eckley, I. A. Optimal detection of changepoints with a linear computational cost. J. Am. Stat. Assoc. 107, 1590–1598. https://doi.org/10.1080/01621459.2012.737745 (2012).Article 
MathSciNet 
CAS 
MATH 

Google Scholar 
Bakun, A. Coastal Upwelling Indices, West Coast of North America. US Department of Commerce. NOAA Technical Report, NMFS SSRF-671 (1973).Di Lorenzo, E. Seasonal dynamics of the surface circulation in the Southern California Current System. Deep-Sea Res. Part II 50(14–16), 2371–2388. https://doi.org/10.1016/S0967-0645(03)00125-5 (2003).Article 
ADS 

Google Scholar 
Lynn, R. J. & Simpson, J. J. The California Current System: The seasonal variability of its physical characteristics. J. Geophys. Res. 92(C12), 12947. https://doi.org/10.1029/jc092ic12p12947 (1987).Article 
ADS 

Google Scholar 
Sinnett, G. & Feddersen, F. The surf zone heat budget: The effect of wave heating. Geophys. Res. Lett. 41(20), 7217–7226. https://doi.org/10.1002/2014GL061398 (2014).Article 
ADS 

Google Scholar 
Wei, X., Li, K.-Y., Kilpatrick, T., Wang, M. & Xie, S.-P. Large-scale conditions for the record-setting Southern California marine heatwave of August 2018. Geophys. Res. Lett. 48(7), e2020GL091803 (2021).Article 
ADS 

Google Scholar 
Freedman, R. M., Brown, J. A., Caldow, C. & Caselle, J. E. Marine protected areas do not prevent marine heatwave-induced fish community structure changes in a temperate transition zone. Sci. Rep. 10(1), 1–8. https://doi.org/10.1038/s41598-020-77885-3 (2020).Article 
CAS 

Google Scholar 
Heupel, M. R., Simpfendorfer, C. A. & Hueter, R. E. Running before the storm: blacktip sharks respond to falling barometric pressure associated with Tropical Storm Gabrielle. J. Fish Biol. 63(5), 1357–1363. https://doi.org/10.1046/J.1095-8649.2003.00250.X (2003).Article 

Google Scholar 
Guttridge, T. L. et al. Deep danger: Intra-specific predation risk influences habitat use and aggregation formation of juvenile lemon sharks Negaprion brevirostris. Mar. Ecol. Progr. Ser. 445, 279–291 (2012).Article 
ADS 

Google Scholar 
Grainger, R. et al. Diet composition and nutritional niche breadth variability in juvenile white sharks (Carcharodon carcharias). Front. Mar. Sci. 7, 422 (2020).Article 

Google Scholar 
Hussey, N. E., Christiansen, H. M. & Dudley, S. F. J. Size-based analysis of diet and trophic position of the white shark, carcharodon carcharias, in South African waters. Glob. Perspect. Biol. Life Hist. White Shark 3, 27–49. https://doi.org/10.1201/b11532-5 (2012).Article 

Google Scholar 
Kim, S. L., Tinker, M. T., Estes, J. A. & Koch, P. L. Ontogenetic and among-individual variation in foraging strategies of northeast Pacific white sharks based on stable isotope analysis. PLoS ONE 7(9), e45068. https://doi.org/10.1371/JOURNAL.PONE.0045068 (2012).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tinker, M. T. et al. Dramatic increase in sea otter mortality from white sharks in California. Mar. Mamm. Sci. 32(1), 309–326. https://doi.org/10.1111/mms.12261 (2015).Article 

Google Scholar  More

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

United Nations. Transforming our world: the 2030 Agenda for Sustainable Development. General Assembly https://doi.org/10.5040/9781782257790.part-008 (2015).Article 

Google Scholar 
European Commission. Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy. Off. J. Eur. Communities L327, 1–72 (2000).
Google Scholar 
Kelly, R. P. et al. Harnessing DNA to improve environmental management. Science (80-) 344, 1455–1456 (2014).ADS 
CAS 

Google Scholar 
Bálint, M. et al. Cryptic biodiversity loss linked to global climate change. Nat. Clim. Chang. 1, 313–318 (2011).ADS 

Google Scholar 
Bohmann, K. et al. Environmental DNA for wildlife biology and biodiversity monitoring. Trends Ecol. Evol. 29, 358–367 (2014).PubMed 

Google Scholar 
Deiner, K. et al. Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Mol. Ecol. 26, 5872–5895 (2017).PubMed 

Google Scholar 
Ficetola, G. F., Miaud, C., Pompanon, F. & Taberlet, P. Species detection using environmental DNA from water samples. Biol. Lett. 4, 423–425 (2008).PubMed 
PubMed Central 

Google Scholar 
Yang, J., Jeppe, K., Pettigrove, V. & Zhang, X. Environmental DNA metabarcoding supporting community assessment of environmental stressors in a field-based sediment microcosm study. Environ. Sci. Technol. https://doi.org/10.1021/acs.est.8b04903 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
DiBattista, J. D. et al. Environmental DNA can act as a biodiversity barometer of anthropogenic pressures in coastal ecosystems. Sci. Rep. 10, 1–15 (2020).
Google Scholar 
Ministry of the Environment. Manual for Water Quality Assessment Method by Aquatic Organisms -Japanese version of average score method-. (2017).Mayama, S. Taxonomic revisions to the differentiating diatom groups for water quality evaluation and some comments for taxa with new designations. Diatom 15, 1–9 (1994).
Google Scholar 
Kobayashi, H. & Mayama, S. Evaluation of river water quality by diatoms. Korean J. Phycol. 4, 121–133 (1989).
Google Scholar 
European Commission. Technical Guidance Document on Risk Assessment Part II. (2003).Wang, P. et al. Environmental DNA: An emerging tool in ecological assessment. Bull. Environ. Contam. Toxicol. 103, 651–656 (2019).CAS 
PubMed 

Google Scholar 
Zhang, X. Environmental DNA shaping a new era of ecotoxicological research. Environ. Sci. Technol. 53, 5605–5612 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Cristescu, M. E. & Hebert, P. D. N. Uses and misuses of environmental DNA in biodiversity science and conservation. Annu. Rev. Ecol. Evol. Syst. 49, 209–230 (2018).
Google Scholar 
Ficetola, G. F., Taberlet, P. & Coissac, E. How to limit false positives in environmental DNA and metabarcoding?. Mol. Ecol. Resour. 16, 604–607 (2016).CAS 
PubMed 

Google Scholar 
Yates, M. C., Derry, A. M. & Cristescu, M. E. Environmental RNA: A revolution in ecological resolution?. Trends Ecol. Evol. 36, 601–609 (2021).CAS 
PubMed 

Google Scholar 
Veilleux, H. D., Misutka, M. D. & Glover, C. N. Environmental DNA and environmental RNA: Current and prospective applications for biological monitoring. Sci. Total Environ. 782, 146891 (2021).ADS 
CAS 
PubMed 

Google Scholar 
Cristescu, M. E. Can Environmental RNA Revolutionize Biodiversity Science?. Trends Ecol. Evol. 34, 694–697 (2019).PubMed 

Google Scholar 
Qian, T., Shan, X., Wang, W. & Jin, X. Effects of Temperature on the Timeliness of eDNA/eRNA: A Case Study of Fenneropenaeus chinensis. Water (Switzerland) 14, 1155 (2022).CAS 

Google Scholar 
Jo, T., Tsuri, K., Hirohara, T., Yamanaka, H. & Toshiaki Jo, C. Warm temperature and alkaline conditions accelerate environmental RNA degradation. Environ. DNA 00, 1–13 (2022).
Google Scholar 
Miyata, K. et al. Fish environmental RNA enables precise ecological surveys with high positive predictivity. Ecol. Indic. 128, 107796 (2021).
Google Scholar 
Littlefair, J. E., Rennie, M. D. & Cristescu, M. E. Environmental nucleic acids: a field-based comparison for monitoring freshwater habitats using eDNA and eRNA. Mol. Ecol. Resour. https://doi.org/10.1111/1755-0998.13671 (2022).Article 
PubMed 

Google Scholar 
Broman, E., Bonaglia, S., Norkko, A., Creer, S. & Nascimento, F. J. A. High throughput shotgun sequencing of eRNA reveals taxonomic and derived functional shifts across a benthic productivity gradient. Mol. Ecol. https://doi.org/10.1111/mec.15561 (2020).Article 
PubMed 

Google Scholar 
Miya, M. et al. Use of a filter cartridge for filtration of water samples and extraction of environmental DNA. J. Vis. Exp. https://doi.org/10.3791/54741 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Oe, S., Sashika, M., Fujimoto, A., Shimozuru, M. & Tsubota, T. Predation impacts of invasive raccoons on rare native species. Sci. Rep. 10, 1–12 (2020).
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 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
MLIT (Ministry of Land Infrastructure and Transport). IV Benthic invertebrate. Manual of National Census of the River Environment (River Edition) (in Japanese). http://www.nilim.go.jp/lab/fbg/ksnkankyo/mizukokuweb/system/DownLoad/H28KK_manual_river/H28KK_02.teisei.pdf (2016).Hleap, J. S., Littlefair, J. E., Steinke, D., Hebert, P. D. N. & Cristescu, M. E. Assessment of current taxonomic assignment strategies for metabarcoding eukaryotes. Mol. Ecol. Resour. 21, 2190–2203 (2021).PubMed 

Google Scholar 
Jones, E. P. et al. Guidance for end users on DNA methods development and project assessment. JNCC Report (2020).Littlefair, J. E., Rennie, M. D. & Cristescu, M. E. Environmental nucleic acids: A field-based comparison for monitoring freshwater habitats using eDNA and eRNA. Mol. Ecol. Resour. 22, 2928–2940. https://doi.org/10.1111/1755-0998.13671 (2022).Article 
CAS 
PubMed 

Google Scholar 
MLIT (Ministry of Land Infrastructure and Transport). River Environmental Database (in Japanese). http://www.nilim.go.jp/lab/fbg/ksnkankyo/ (2018).Kitahashi, T. et al. Meiofaunal diversity at a seamount in the Pacific Ocean: A comprehensive study using environmental DNA and RNA. Deep. Res. Part I Oceanogr. Res. Pap. 160, 103253. https://doi.org/10.1016/j.dsr.2020.103253 (2020).Article 

Google Scholar 
Brandt, M. I. et al. An assessment of environmental metabarcoding protocols aiming at favoring contemporary biodiversity in inventories of deep-sea communities. Front. Mar. Sci. 7, 234 (2020).
Google Scholar 
Laroche, O. et al. A cross-taxa study using environmental DNA / RNA metabarcoding to measure biological impacts of off shore oil and gas drilling and production operations. Mar. Pollut. Bull. 127, 97–107 (2018).CAS 
PubMed 

Google Scholar 
Laroche, O. et al. Metabarcoding monitoring analysis: The pros and cons of using co-extracted environmental DNA and RNA data to assess offshore oil production impacts on benthic communities. PeerJ 5, e3347 (2017).PubMed 
PubMed Central 

Google Scholar 
Pochon, X. et al. Wanted dead or alive ? Using metabarcoding of environmental DNA and RNA to distinguish living assemblages for biosecurity applications. PLoS ONE 12, 1–19 (2017).
Google Scholar 
Foley, C. J., Bradley, D. L. & Höök, T. O. A review and assessment of the potential use of RNA: DNA ratios to assess the condition of entrained fish larvae. Ecol. Indic. 60, 346–357 (2016).CAS 

Google Scholar 
Guardiola, M. et al. Spatio-temporal monitoring of deep-sea communities using metabarcoding of sediment DNA and RNA. PeerJ 4, 1–31 (2016).
Google Scholar 
Keeley, N., Wood, S. A. & Pochon, X. Development and preliminary validation of a multi-trophic metabarcoding biotic index for monitoring benthic organic enrichment. Ecol. Indic. 85, 1044–1057 (2018).CAS 

Google Scholar 
Whangbo, J. S. & Hunter, C. P. Environmental RNA interference. Trends Genet. 24, 297–305 (2008).CAS 
PubMed 

Google Scholar 
Sidova, M., Tomankova, S., Abaffy, P., Kubista, M. & Sindelka, R. Effects of post-mortem and physical degradation on RNA integrity and quality. Biomol. Detect. Quantif. 5, 3–9 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wood, S. A. et al. Release and degradation of environmental DNA and RNA in a marine system. Sci. Total Environ. 704, 135314 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Watanabe, T. Picture Book and Ecology of the Freshwater Diatoms (UCHIDA ROKAKUHO PUBLISHING CO., LTD., 2005).
Google Scholar 
Laroche, O. et al. First evaluation of foraminiferal metabarcoding for monitoring environmental impact from an offshore oil drilling site. Mar. Environ. Res. 120, 225–235 (2016).CAS 
PubMed 

Google Scholar 
Andruszkiewicz Allan, E. et al. Environmental DNA shedding and decay rates from diverse animal forms and thermal regimes. Environ. DNA 3, 492–514 (2021).
Google Scholar 
Hajibabaei, M. et al. Watered-down biodiversity? A comparison of metabarcoding results from DNA extracted from matched water and bulk tissue biomonitoring samples. PLoS ONE 14, e0225409 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
the Orthopterological Society of Japan. Orthoptera of the Japanese archipelago in color (Hokkaido University Press, 2006).Li, Z. H. et al. Enzymatic alterations and RNA/DNA ratio in intestine of rainbow trout, Oncorhynchus mykiss, induced by chronic exposure to carbamazepine. Ecotoxicology 19, 872–878 (2010).CAS 
PubMed 

Google Scholar 
Chícharo, M. A. & Chícharo, L. RNA:DNA ratio and other nucleic acid derived indices in marine ecology. Int. J. Mol. Sci. 9, 1453–1471 (2008).PubMed 
PubMed Central 

Google Scholar 
Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).CAS 
PubMed 

Google Scholar 
Hebert, P. D. N., Stoeckle, M. Y., Zemlak, T. S. & Francis, C. M. Identification of birds through DNA barcodes. PLoS Biol. 2, e312 (2004).PubMed 
PubMed Central 

Google Scholar 
Takenaka, M., Yano, K., Suzuki, T. & Tojo, K. Development of novel PCR primer sets for DNA metabarcoding of aquatic insects, and the discovery of some cryptic species. bioRxiv https://doi.org/10.1101/2021.11.05.467390 (2021).Article 

Google Scholar 
Elbrecht, V. et al. Testing the potential of a ribosomal 16S marker for DNA metabarcoding of insects. PeerJ 4, 1966 (2016).
Google Scholar 
Leese, F. et al. Improved freshwater macroinvertebrate detection from environmental DNA through minimized nontarget amplification. Environ. DNA 3, 261–276 (2021).CAS 

Google Scholar 
Hajibabaei, M., Porter, T. M., Wright, M. & Rudar, J. COI metabarcoding primer choice affects richness and recovery of indicator taxa in freshwater systems. PLoS ONE 14, e0220953 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Elbrecht, V. et al. Validation of COI metabarcoding primers for terrestrial arthropods. PeerJ 7, 7745 (2019).
Google Scholar 
Elbrecht, V. & Leese, F. Can DNA-based ecosystem assessments quantify species abundance? Testing primer bias and biomass-sequence relationships with an innovative metabarcoding protocol. PLoS ONE 10, e0130324 (2015).PubMed 
PubMed Central 

Google Scholar 
Marquina, D., Andersson, A. F. & Ronquist, F. New mitochondrial primers for metabarcoding of insects, designed and evaluated using in silico methods. Mol. Ecol. Resour. 19, 90–104 (2019).CAS 
PubMed 

Google Scholar 
Tochigi prefectural government. Results of continuous monitoring and measurement [Water quality] in Japanese. https://www.pref.tochigi.lg.jp/d03/eco/kankyou/hozen/joujikanshikekka-mizu.html (2020).Emilson, C. E. et al. DNA metabarcoding and morphological macroinvertebrate metrics reveal the same changes in boreal watersheds across an environmental gradient. Sci. Rep. 7, 1–11 (2017).CAS 

Google Scholar 
Uchida, N., Kubota, K., Aita, S. & Kazama, S. Aquatic insect community structure revealed by eDNA metabarcoding derives indices for environmental assessment. PeerJ 2020, e9176 (2020).
Google Scholar 
Baird, D. J. & Hajibabaei, M. Biomonitoring 2.0: a new paradigm in ecosystem assessment made possible by next-generation DNA sequencing. Mol. Ecol. 21, 2039–2044 (2012).PubMed 

Google Scholar  More

Marra, P., Hobson, K. A. & Holmes, R. T. Linking winter and summer events in a migratory bird by using stable-carbon isotopes. Science 282, 1884–1886 (1998).ADS 
CAS 
PubMed 

Google Scholar 
Korslund, L. & Steen, H. Small rodent winter survival: Snow conditions limit access to food resources. J. Anim. Ecol. 75, 423–436 (2009).
Google Scholar 
Both, C., Bouwhuis, S., Lessells, C. M. & Visser, M. E. Climate change and population declines in a long-distance migratory bird. Nature 441, 81–83 (2006).ADS 
CAS 
PubMed 

Google Scholar 
Rughetti, M. & Festa-Bianchet, M. Effects of spring–summer temperature on body mass of chamois. J. Mammal. 93, 1301–1307 (2012).
Google Scholar 
Davidson, J. & Andrewartha, H. The influence of rainfall, evaporation and atmospheric temperature on fluctuations in the size of a natural population of Thrips imaginis (Thysanoptera). J. Anim. Ecol. 17, 200–222 (1948).
Google Scholar 
Sillett, T. S., Holmes, R. T. & Sherry, T. W. Impacts of a global climate cycle on population dynamics of a migratory songbird. Science 288, 2040–2043 (2000).ADS 
CAS 
PubMed 

Google Scholar 
SÆther, B. E., Sutherland, W. J. & Engen, S. Climate influences on avian population dynamics. Adv. Ecol. Res. 35, 185–209 (2004).
Google Scholar 
Frederiksen, M., Daunt, F., Harris, M. & Wanless, S. The demographic impact of extreme events: Stochastic weather drives survival and population dynamics in a long-lived seabird. J. Anim. Ecol. 77, 1020–1029 (2008).CAS 
PubMed 

Google Scholar 
Cox, A. R., Robertson, R. J., Rendell, W. B. & Bonier, F. Population decline in tree swallows (Tachycineta bicolor) linked to climate change and inclement weather on the breeding ground. Oecologia 192, 713–722 (2020).ADS 
PubMed 

Google Scholar 
Peach, W., Baillie, S. & Underhill, L. Survival of British Sedge Warblers in relation to west African rainfall. Ibis 133, 300–305 (1991).
Google Scholar 
Altwegg, R., Dummermuth, S., Anholt, B. R. & Flatt, T. Winter weather affects asp viper Vipera aspis population dynamics through susceptible juveniles. Oikos 110, 55–66 (2005).
Google Scholar 
Woodworth, B. K., Wheelwright, N. T., Newman, A. E., Schaub, M. & Norris, D. R. Winter temperatures limit population growth rate of a migratory songbird. Nat. Commun. 8, 14812 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ådahl, E., Lundberg, P. & Jonzén, N. From climate change to population change: The need to consider annual life cycles. Glob. Change Biol. 12, 1627–1633 (2006).ADS 

Google Scholar 
Marra, P. P., Cohen, E. B., Loss, S. R., Rutter, J. E. & Tonra, C. M. A call for full annual cycle research in animal ecology. Biol. Lett. 11, 20150552 (2015).PubMed 
PubMed Central 

Google Scholar 
Telenský, T., Klvaňa, P., Jelínek, M., Cepák, J. & Reif, J. The influence of climate variability on demographic rates of avian Afro-palearctic migrants. Sci. Rep. 10, 17592 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Dybala, K. E., Eadie, J. M., Gardali, T., Seavy, N. E. & Herzog, M. P. Projecting demographic responses to climate change: Adult and juvenile survival respond differently to direct and indirect effects of weather in a passerine population. Glob. Chang. Biol. 19, 2688–2697 (2013).ADS 
PubMed 

Google Scholar 
Gullett, P., Evans, K. L., Robinson, R. A. & Hatchwell, B. J. Climate change and annual survival in a temperate passerine: Partitioning seasonal effects and predicting future patterns. Oikos 123, 389–400 (2014).
Google Scholar 
Selwood, K. E., McGeoch, M. A. & Mac Nally, R. The effects of climate change and land-use change on demographic rates and population viability. Biol. Rev. 90, 837–853 (2015).PubMed 

Google Scholar 
Bridge, E. S. et al. Technology on the move: Recent and forthcoming innovations for tracking migratory birds. Bioscience 61, 689–698 (2011).
Google Scholar 
van Bemmelen, R. S. A. et al. Red-necked phalaropes in the Western Palearctic reveals contrasting migration and wintering movement strategies. Front. Ecol. Evol. 7, 86 (2019).
Google Scholar 
Jiguet, F. et al. Unravelling migration connectivity reveals unsustainable hunting of the declining ortolan bunting. Sci. Adv. 5, eaau2642 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Stutchbury, B. J. M. et al. Tracking long-distance songbird migration by using geolocators. Science 323, 896 (2009).ADS 
CAS 
PubMed 

Google Scholar 
Sanderson, F. J., Donald, P. F., Pain, D. J., Burfield, I. J. & Van Bommel, F. P. J. Long-term population declines in Afro-Palearctic migrant birds. Biol. Conserv. 131, 93–105 (2006).
Google Scholar 
Sandvik, H., Erikstad, K. E., Barrett, R. T. & Yoccoz, N. G. The effect of climate on adult survival in five species of North Atlantic seabirds. J. Anim. Ecol. 74, 817–831 (2005).
Google Scholar 
BirdLife International and NatureServe. Bird species distribution maps of the world. (2014).Hedenström, A., Klaassen, R. H. G. & Åkesson, S. Migration of the little ringed plover Charadrius dubius breeding in South Sweden tracked by geolocators. Bird Study 60, 466–474 (2013).
Google Scholar 
Fransson, T., Österblom, H. & Hall-Karlsson, S. Svensk ringmärkningsatlas. (2008).Pakanen, V., Lampila, S., Arppe, H. & Valkama, J. Estimating sex specific apparent survival and dispersal of Little Ringed Plovers (Charadrius dubius). Ornis Fenn. 92, 52 (2015).
Google Scholar 
Jarošík, V., Honěk, A., Magarey, R. & Skuhrovec, J. Developmental database for phenology models: Related insect and mite species have similar thermal requirements. J. Econ. Entomol. 104, 1870–1876 (2011).PubMed 

Google Scholar 
Cramp, J. Handbook of the Birds of Europe, the Middle East and North Africa (Oxford University Press, 1992).
Google Scholar 
Leyrer, J. et al. Mortality within the annual cycle: Seasonal survival patterns in Afro-Siberian Red Knots Calidris canutus canutus. J. Ornithol. 154, 933–943 (2013).
Google Scholar 
Norris, R. D. & Marra, P. P. Seasonal interactions, habitat quality, an population dynamics in migratory birds. Condor 109, 535–547 (2007).
Google Scholar 
Schmaljohann, H., Eikenaar, C. & Sapir, N. Understanding the ecological and evolutionary function of stopover in migrating birds. Biol. Rev. 97, 1231–1252 (2022).PubMed 

Google Scholar 
Doyle, S. et al. Temperature and precipitation at migratory grounds influence demographic trends of an Arctic-breeding bird. Glob. Change Biol. 26, 5447–5458 (2020).ADS 

Google Scholar 
Rockwell, S. M. et al. Seasonal survival estimation for a long-distance migratory bird and the influence of winter precipitation. Oecologia 183, 715–726 (2017).ADS 
PubMed 

Google Scholar 
Insley, H., Peach, W., Swann, B. & Etheridge, B. Survival rates of Redshank Tringa totanus wintering on the Moray Firth. Bird Study 44, 277–289 (1997).
Google Scholar 
Duriez, O., Ens, B. J., Choquet, R., Pradel, R. & Klaassen, M. Comparing the seasonal survival of resident and migratory oystercatchers: Carry-over effects of habitat quality and weather conditions. Oikos 121, 862–873 (2012).
Google Scholar 
Cook, A. S. C. P. et al. Temperature and density influence survival in a rapidly declining migratory shorebird. Biol. Conserv. 260, 109198 (2021).
Google Scholar 
Pearce-Higgins, J. W., Yalden, D., Dougall, T. & Beale, C. M. Does climate change explain the decline of a trans-Saharan Afro-Palaearctic migrant?. Oecologia 159, 649–659 (2009).ADS 
CAS 
PubMed 

Google Scholar 
Weiser, E. L. et al. Environmental and ecological conditions at Arctic breeding sites have limited effects on true survival rates of adult shorebirds. Auk 135, 29–43 (2018).
Google Scholar 
Piersma, T. & Baker, A. Life history characteristics and the conservation of migratory shorebirds. In Behaviour and Conservation (eds Gosling, L. & Sutherland, W.) 105–124 (Cambridge University Press, 2000).
Google Scholar 
Conklin, J. R., Senner, N. R., Battley, P. F. & Piersma, T. Extreme migration and the individual quality spectrum. J. Avian Biol. 48, 19–36 (2017).
Google Scholar 
Méndez, V., Alves, J. A., Gill, J. A. & Gunnarsson, T. G. Patterns and processes in shorebird survival rates: A global review. Ibis (Lond.) 160, 723–741 (2018).
Google Scholar 
Roche, E. A. et al. Range-wide piping plover survival: Correlated patterns and temporal declines. J. Wildl. Manage. 74, 1784–1791 (2010).
Google Scholar 
Skagen, S. K. & Knopf, F. L. Toward conservation of midcontinental shorebird migrations. Conserv. Biol. 7, 533–541 (1993).
Google Scholar 
Kasahara, S., Moritomo, G., Kitamura, W., Imanishi, S. & Azuma, N. Rice fields along the East Asian-Australasian flyway are important habitats for an inland wader’s migration. Sci. Rep. 10, 4118 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Studds, C. E. & Marra, P. P. Linking fluctuations in rainfall to nonbreeding season performance in a long-distance migratory bird, Setophaga ruticilla. Clim. Res. 35, 115–122 (2007).
Google Scholar 
Newton, I. Can conditions experienced during migration limit the population levels of birds?. J. Ornithol. 147, 146–166 (2006).
Google Scholar 
Anderson, A. M. et al. Drought at a coastal wetland affects refuelling and migration strategies of shorebirds. Oecologia 197, 661–674 (2021).ADS 
PubMed 
PubMed Central 

Google Scholar 
Rakhimberdiev, E. et al. Fuelling conditions at staging sites can mitigate Arctic warming effects in a migratory bird. Nat. Commun. 9, 4263 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Wikelski, M. et al. Costs of migration in free-flying songbirds. Nature 423, 704 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Meissner, W. Ageing and sexing the curonicus subspecies of the Little Ringed Plover Charadrius dubius. Wader Study Gr. Bull. 113, 28–31 (2007).
Google Scholar 
Giorgi, F. Climate change hot-spots. Geophys. Res. Lett. 33, L08707 (2006).ADS 

Google Scholar 
Almazroui, M., Saeed, S., Saeed, F., Islam, M. N. & Ismail, M. Projections of precipitation and temperature over the South Asian countries in CMIP6. Earth Syst. Environ. 4, 297–320 (2020).ADS 

Google Scholar 
Lisovski, S. et al. The Indo-European flyway: Opportunities and constraints reflected by Common Rosefinches breeding across Europe. J. Biogeogr. 48, 1255–1266 (2021).
Google Scholar 
Lislevand, T. et al. Red-spotted Bluethroats Luscinia s. svecica migrate along the Indo-European flyway: A geolocator study. Bird Study 62, 508–515 (2015).
Google Scholar 
Brlík, V., Ilieva, M., Lisovski, S., Voigt, C. C. & Procházka, P. First insights into the migration route and migratory connectivity of the Paddyfield Warbler using geolocator tagging and stable isotope analysis. J. Ornithol. 159, 879–882 (2018).
Google Scholar 
Wernham, C. et al. The Migration Atlas: Movements of the Birds of Britain and Ireland (Poyser, 2002).
Google Scholar 
Saurola, P., Valkama, J. & Velmala, W. The Finnish Bird Ringing Atlas (Finnish Museum of Natural History and the Ministry of Environment, 2013).
Google Scholar 
Bairlein, F. et al. Atlas des Vogelzugs—Ringfunde Deutscher Brut- und Gastvögel (AULA-Verlag GmbH, 2014).
Google Scholar 
Salewski, V., Hochachka, W. M. & Fiedler, W. Multiple weather factors affect apparent survival of European Passerine birds. PLoS One 8, e59110 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Schaub, M., Jakober, H. & Stauber, W. Demographic response to environmental variation in breeding, stopover and non-breeding areas in a migratory passerine. Oecologia 167, 445–459 (2011).ADS 
PubMed 

Google Scholar 
Brlík, V. et al. Weak effects of geolocators on small birds: A meta-analysis controlled for phylogeny and publication bias. J. Anim. Ecol. 89, 207–220 (2020).PubMed 

Google Scholar 
Weiser, E. L. et al. Effects of geolocators on hatching success, return rates, breeding movements, and change in body mass in 16 species of Arctic-breeding shorebirds. Mov. Ecol. 4, 12 (2016).PubMed 
PubMed Central 

Google Scholar 
Lisovski, S., Sumner, M. D., & Wotherspoon, S. J. TwGeos: Basic data processing for light based geolocation archival tags. 2015. https://github.com/slisovski/TwGeosLisovski, S. & Hahn, S. GeoLight—processing and analysing light-based geolocator data in R. Methods Ecol. Evol. 3, 1055–1059 (2012).
Google Scholar 
Ekstrom, P. A. An advance in geolocation by light. Mem. Natl Inst. Polar Res. 58, 210–226 (2004).
Google Scholar 
Brunsdon, C. & Chen, H. GISTools: Some further GIS capabilities for R. (2014).Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).PubMed 
PubMed Central 

Google Scholar 
Wang, B. The Asian Monsoon (Springer, 2006).
Google Scholar 
R Core Team. A Language and Environment for Statistical Computing (2021).Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).ADS 

Google Scholar 
Lebreton, J., Burnham, K. P., Clobert, J. & Anderson, D. R. Modeling survival and testing biological hypotheses using marked animals: A unified approach with case studies. Ecol. Monogr. 62, 67–118 (1992).
Google Scholar 
White, G. C. & Burnham, K. P. Program MARK: Survival estimation from populations of marked animals. Bird Study 46, S120–S139 (1999).
Google Scholar 
Pradel, R. Flexibility in survival analysis from recapture data: Handling trap-dependence. In Marked Individuals in the Study of Bird Population (eds Lebreton, J.-D. & North, P.) (Birkhäuser-Verlag, 1993).
Google Scholar 
Choquet, R., Lebreton, J. D., Gimenez, O., Reboulet, A. M. & Pradel, R. U-CARE: Utilities for performing goodness of fit tests and manipulating CApture-REcapture data. Ecography (Cop.) 32, 1071–1074 (2009).
Google Scholar 
Pakanen, V. M. et al. Natal dispersal does not entail survival costs but is linked to breeding dispersal in a migratory shorebird, the southern dunlin Calidris alpina schinzii. Oikos 2022, ee08951 (2022).Article 

Google Scholar 
Burnham, K. & Anderson, D. Model Selection and Multimodel Inference: A Practical in-Formation-Theoretic Approach (Springer, 2002).MATH 

Google Scholar 
Grosbois, V. et al. Assessing the impact of climate variation on survival in vertebrate populations. Biol. Rev. 83, 357–399 (2008).CAS 
PubMed 

Google Scholar 
Brlík, V. et al. Survival fluctuations linked to variation in the South Asian monsoon in a Palearctic migratory shorebird. Zenodo https://doi.org/10.5281/zenodo.7026440 (2022). More

ProductivityWith regard to productivity, in the summer harvest of the 2016–2017 crop year, in which all grain production systems had soybean in common, there were significant differences among crop rotations with species diversification and the double-cropped corn–soybean rotation; performance was better in AS-II, AS-III, AS-IV, AS-V and AS-VI and worst in AS-I. There was no significant difference in productivity among the crop rotations with species diversification (Table 2).Table 2 Productivity (kg ha−1) of the crop rotation systems for the 2014–2015 to 2019–2020 crop years in Londrina, state of Paraná, Brazil.Full size tableFor the summer harvest of the 2019–2020 crop year, in which all the grain production systems again had soybean in common, significant differences were also observed among the production systems. AS-I and AS-V had the lowest productivities, differing from AS-IV and AS-VI, which had the highest productivities. Conversely, the productivities of AS-II and AS-III did not differ significantly from those of the other evaluated systems (Table 2).In the cycle that ended in crop year 2019–2020, compared to the cycle that ended in crop year 2016–2017, there was a reduction in soybean productivity in all the analyzed grain production systems (Table 2). There was also a decrease in the productivity of corn grown in the summer in the 2015–2016 and 2018–2020 crop years. This decrease in productivity observed between the production cycles may be associated with climatic conditions because from 2014–2015 to 2016–2017, there was a good rainfall distribution and few water deficit peaks, while from 2017–2018 to 2019–2020, the water deficit peaks were more constant, especially in 2018–2019 and 2019–2020 (Fig. 1). Notably, there was a greater influence of the El Niño phenomenon on the first production cycle (2014–2017) and of the La Niña phenomenon on the second (2017–2020)28. In southern Brazil, these phenomena correspond to periods of weaker droughts under El Niño conditions and a higher frequency of severe and moderate droughts under La Niña conditions29. The occurrence of a water deficit may limit plant growth and development, particularly during the flowering and grain filling stages. Systems that employ crop rotation with species diversification are less susceptible to production losses due to water deficits30. The results of this study show that crop rotation systems with species diversification, by providing a longer soil cover time for soil protection, either with live plants or from the input of surface straw, together with the respective increase in the soil water storage capacity, can mitigate productivity losses resulting from periods of drought (Fig. 1, Table 2).Another finding is that soybean has higher productivity when grown in systems with greater species diversification, as was the case for AS-IV and AS-VI (Table 2). In general, grain production systems that employ crop rotation with species diversification produce more than those that are not diversified31,32, especially in atypical growing seasons affected by climatic factors limiting crop development33.AS-I and AS-V showed the lowest soybean productivity at the end of the second crop rotation cycle, in the 2019–2020 crop year (Table 2). AS-I had the lowest soybean productivity at the end of the two crop cycles, i.e., in 2016–2017 and 2019–2020, a result that is directly related to corn–soybean double cropping. In the southern region of Brazil, for example, soybean productivity in crop rotation systems with species diversification is 6.2% higher than that in double-crop systems22. In this sense, the results of this study indicate that production systems with little species diversification have lower soybean productivity than those that employ crop rotation with species diversification.At the end of the second crop rotation cycle, in 2019–2020, AS-II and AS-III also showed good soybean productivity, i.e., 3864 kg ha−1 and 3848 kg ha−1, respectively. AS-III had one of the highest grain yields in the summer crops, which may be associated with the use of cover crops in the previous winter. The use of cover crops in the winter growing seasons results in a number of benefits from permanent soil cover because cover crops can improve chemical, physical and biological soil attributes, favoring the accumulation of biomass and organic carbon in the soil34 and prevent soil erosion35. In addition, cover crops control pests, diseases and weeds36 and contribute to weed37 and nematode38 control.Regarding crop dry matter, AS-III, AS-IV, AS-V and AS-VI (Table 3) deposited the most dry matter in the system; the crop dry matter in these systems was greater than that in AS-I and showed no significant difference in relation to that in AS-II. The lower production of dry matter in AS-I is explained by the lack of corn cultivation in the summer. Corn grown in the summer was the crop that most contributed to the accumulation of dry matter in AS-III, AS-IV and AS-VI, compensating for the low averages obtained with beans in AS-V and AS-VI and with safflower in AS-IV. The higher dry matter inputs in AS-IV and AS-VI are because these are the only systems in which corn was grown in the summer for two consecutive years. The average dry matter contributed by corn grown in the summer is 9.9 Mg ha−1, while that from off-season corn and soybeans is 6.5 Mg ha−1 and 4.35 Mg ha−1, respectively.Table 3 Dry matter (Mg ha−1) of the grain production systems for the 2014–2015 to 2019–2020 crop years in Londrina, state of Paraná, Brazil.Full size tableStudies carried out in the Cerrado, Mato Grosso, showed that the minimum amount of plant dry matter deposited by crop rotation systems needed to obtain a balance of C in the soil in the region is between 11.7 and 13.3 Mg ha−139. Therefore, we can deduce that AS-III, AS-IV, AS-V and AS-VI would enter equilibrium; that is, over time, there will be neither accumulation of nor loss of C from the soil. For AS-I and AS-II, we can conclude that over time, C stocks in the soil will be reduced, causing a loss of soil fertility and, consequently, productivity, as shown in Table 2, where the yield of AS-I was lower than that of the most diversified treatments.The results show that crop diversification in grain production systems with the cultivation of commercial or cover crops in the winter benefited soybean and corn production in the summer. In similar studies, species diversification is reported to have increased summer crop productivity over time; specifically, in the U.S. and Canada, corn productivity increased by an average of 28.1%40, and in Canada, corn yield increased by 9.9% and soybean productivity increased by 11.8%41.Economic analysisThe highest mean annual revenue was found for AS-VI, while the lowest was found for AS-III. Regarding the mean annual cost, AS-VI demanded the greatest investment, while AS-III showed the lowest production cost. The highest mean annual profit was also observed for AS-VI, highlighting that the revenue more than offset the costs. As expected, the lowest mean annual profit was found for AS-I, that is, the corn–soybean double-crop system (Fig. 2).Figure 2(a) Mean annual revenue, (b) mean annual cost and (c) mean annual profit of grain production systems with varied levels of species diversity in Londrina, state of Paraná, Brazil.Full size imageThe higher profitability observed for AS-VI indicates that the practice of crop rotation with species diversification in grain production systems increased the grain productivity and economic gains. In this system, the productivity of the commercial crops was positively impacted, and the crops showed excellent yields compared to those in the production systems with lower species diversification. In addition, the winter crops played a key role in the composition of the revenues, especially wheat and bean. As previously noted, the highest mean annual costs of inputs (US$ 685), agricultural operations (US$ 353) and other costs (US$ 177) were found for this system. Within the inputs, the highest cost was for fertilizers (K2O, P2O5, and N), accounting for approximately 22% of the total cost (US$ 280). The higher cost may be related to higher energy demands because in a grain production system, a greater energy volume represents a greater use of inputs42. However, although the cost was the highest, the system was found to be more capable of converting investments into higher productivity and, consequently, into higher revenue and profit. Other studies conducted in Brazil also found economic benefits in crop rotation systems with species diversification, for example, in areas with a predominance of Caiuá sandstone, a region with low-fertility soils, in which the highest profitability was obtained in diversified systems that adopted the highest number of commercial crops, both in the winter and summer growing seasons21. Similarly, in another study in southern Brazil, higher productivities were obtained for more diversified crop rotation systems23. In a long-term study involving soybean, corn, wheat and tropical forage grasses in southern Brazil, higher profits were also found for more diversified production systems22.AS-II had the second highest mean annual profit; this system is characterized by the cultivation of cereals in the winter. The results show that this grain production system is promising, as the use of winter cereal crops had a positive effect on the productivity of the summer crops, leading to increased revenue and profit from the sale of soybean and corn (Supplementary Table S2). With regard to costs, the items that generated the highest expenses in AS-II were inputs, accounting for an average of 54% of the total cost, followed by agricultural operations, which represented an average of 31% of the total, and other costs, accounting for an average of 15% of the total cost (Supplementary Table S2). Studies conducted in other locations also recommend crop rotation systems with the use of cereals, as in the semiarid Northern Great Plains, Canada, where higher productivity and greater profit were found with these cultivation systems compared to a system without species diversification43.AS-V had the third highest mean annual profit. This system is composed of six different crops, and its profitability results were also relevant. Regarding the revenues obtained in the winter growing seasons, beans stood out, accounting for 21% of the total (Supplementary Table S2). One of the problems with AS-V was the cultivation of buckwheat, which, in addition to having a low market price and generating little revenue, also had a high production cost, negatively impacting the entire production system. Thus, if buckwheat had not been cultivated, AS-V could have achieved higher profitability than that observed. With regard to the costs for AS-V, the cost of inputs represented an average of 53% of the total cost, followed by agricultural operations (on average, 31% of the total cost) and other costs (on average, 15% of the total). The cultivation of legumes such as beans in the winter is beneficial for grain production systems because it can favor increased production and, consequently, the profit obtained with subsequent crops44.AS-III had the fourth highest mean annual profit. Although this system did not have the best profitability, it should not be disregarded. This system is focused on the production of straw in the winter and on the revenue generated by the summer crops. However, although cover crops do not generate income for the producer, they indirectly promote gains in subsequent crops. With the maintenance of soil cover, productivity gains and increased revenue are expected in production systems in the medium and long terms21. Cover crops, in general, control pests, diseases and weeds and improve soil conditions36 because they prevent soil compaction and improve soil water infiltration and retention, density, and hydraulic conductivity45. AS-III also had the lowest mean annual production cost; the cost with inputs was on average 35% lower than that observed in the other systems. The lower costs are because the cover crops were not harvested because their benefits are obtained from the biomass generated; thus, the cost is lower than that for systems for which the purpose is to sell grains. One of the great benefits of adopting this system is that the cultivation of cover crops in the winter can reduce the cost of the crop that follows because the amount of inputs involved in the production of the next crop can decrease, as can fuel expenses46. In addition, the lower demand for pesticides makes the system more economical and sustainable and less risky. The quantification and analysis of the items composing the costs of each system are extremely important for producers’ decision-making. However, this analysis requires extreme caution because higher production costs do not necessarily mean lower yields, and similarly, lower costs do not necessarily mean higher profits20,21.AS-IV had the second lowest mean annual profit. This system included winter agroenergy crops. With the exception of canola, the other agroenergy crops grown in this production system showed low profitability. Despite having one of the lowest production costs, the low revenue obtained with agroenergy crops compromised the profitability of AS-IV. Even with the sale of crambe, safflower and canola, the revenues were not sufficient to cover the production costs. Although this system did not show one of the best results, studies with bioenergy crops are being conducted in various regions of the world, and these crops may become an option for southern Brazil, as in the case of Italy, where plants of the family Brassicaceae are being introduced in rotation with cereals as a source of income diversification47.The lowest mean annual profit was observed for AS-I. The low profit is related to the high production costs. Despite having the second highest mean annual revenue, the high production cost compromised the profitability of the system. This result is associated with the lower grain productivity observed in this production system and the fact that it specialized in few crops and focused only on commodities, which are subject to changes in their sale price due to seasonality and market uncertainties, or with the increased susceptibility of this system to problems caused by climatic variations. The crops grown in this system are traded in the international market, and in this case, the producers are only “price takers,”, i.e., they are not able to influence the price of the products48. The prices of commodities may vary; thus, producers may obtain higher or lower revenue due to market fluctuations or volatility. In turn, market fluctuations or volatility are caused by, among other factors, production or external factors, such as exchange rate variations or increased food consumption49,50. AS-I had the highest mean annual pesticide costs, approximately 21% of the total cost (US$ 254). In addition to economic factors, the double-crop system has also generated problems such as the proliferation of pests, diseases and weeds because, in contrast to crop rotation, it does not interrupt the life cycles of pests and diseases51. To control the proliferation of pests, diseases and weeds, the increased use of inputs and an increase in the number of agricultural operations are required52, with a consequent increase in production costs20. This increase in production costs can be observed for winter corn crops, which were more expensive than summer soybean crops. In this system, the mean cost to produce soybean in the summer was US$ 567 per ha, and that to produce corn in the winter was US$ 648. Compared to the other systems studied, the average investment required for the winter growing season was US$ 448 and that for the growing season was US$ 640; that is, the winter crops required 30% less investment than the summer crops (Supplementary Table S3).When considering the real selling price of grains, the highest accumulated profit was observed in AS-VI (Fig. 3); however, in a scenario in which the price of soybeans fluctuates (Fig. 3a) both upward and downward, sensitivity analysis revealed different behaviors. If there was a 44% increase in the selling price of soybeans, the ranking order of the systems would change, making AS-I more profitable. AS-I is the most sensitive to soybean price variations, since in this system, the crop is mainly responsible for generating income and is cultivated in all summers. Thus, the opposite results are also expected. A negative variation in the selling price of soybeans will make AS-I the system with the highest accumulated loss. Price changes can significantly increase or decrease the profitability of producers. Thus, the choice of crops and the number of times a crop appears in each agricultural system determines the profitability of the system as the sale price of the crops varies.Figure 3Price sensitivity analysis (accumulated profit of 6 crop years on the y-axis) of six agricultural systems in Londrina, state of Paraná, Brazil. (a) Soybean; (b) corn; (c) wheat; and (d) bean.Full size imageCorn showed some changes in the order of classification of the systems (Fig. 3b). If the corn sale prices were increased by up to 50%, AS-VI would continue to be the system with the highest accumulated profit. In this scenario, AS-I, composed solely of the corn crop in winter, would cease to be the system with the lowest accumulated profit, occupying the position of AS-III. Different from what happened with the soybean crop, the fluctuations in the corn sale price had less impact on AS-I in terms of accumulated profit. This was because the corn produced in this system accounted for a smaller share of profits and, in some cases, even resulted in losses.Regarding the wheat crop (Fig. 3c), changes in the sale price led to little change in the accumulated profit. Wheat was grown only in AS-II and AS-VI, and in a scenario that considered only the variation in the price of this grain, if its selling price was reduced by up to 47%, AS-VI would continue to be the system with the highest accumulated profit. Changes in the selling price of the bean crop (Fig. 3d) had greater impacts. A 50% increase in the sale price of beans led to a 47% increase in profit in AS-VI.In addition to variations in sale prices, another possible scenario is that crops are stored and sold at later dates. This is possible, as cooperatives are able to provide producers with storage and future sale of grains, extending the time for decision-making. Thus, producers can market products at an optimal time, e.g., when sale prices are better than those on the day of harvest. In this scenario, if corn and soybeans were stored and sold at peak prices recorded each quarter, over the 12 months following the harvest date, the evaluated agricultural systems would show even greater profits. Figure 4 shows the evolution of real prices in tons (USD) of corn and soybeans from July 2014 to March 2021.Figure 4Evolution of corn and soybean prices from July 2014 to March 2020. Data were obtained from the Department of Rural Economy of the Paraná State Secretariat of Agriculture and Supply (DERAL-SEAB). The monetary values are corrected for inflation according to the Brazilian Extended National Consumer Price Index (IPCA) to December 2021.Full size imageIf the sale of soybean and corn was carried out at times of price peaks, the accumulated profit of the systems would vary (Table 4). AS-I, composed exclusively of corn and soybean crops, would become the highest profit system (US$ 3,683). AS-VI, although no longer the highest profit system, would still be one of the systems with the best economic results (US$ 3479). In this scenario, AS-IV would occupy the last position, with the lowest accumulated profit (US$ 2732).Table 4 Profit (USD ha−1) of the grain production systems for the 2014–2015 to 2019–2020 crop years, considering quarterly price peaks in Londrina, state of Paraná, Brazil. .Full size tableIn this scenario, driven by the devaluation of the real against the dollar, the increase in domestic consumption and exports influenced the supply of grains in the market, and agricultural commodities such as soybeans and corn reached high sale values. Thus, it is evident that the market is able to condition the farmer’s profitability, which can influence the results of the analysis, both positively and negatively, according to the daily variations in grain commercialization prices53.From the results, it is evident that species diversification in crop rotation has enabled an increase in both grain productivity and economic gains. It is not enough to simply adopt no-till practices without species diversification in grain production systems31,32; it is necessary for the systems to be aligned with the no-tillage system and conservation agriculture principles. The main reasons for investing in crop diversification are as follows: production of roots and straw to cover the soil surface; improved soil structure and sustained soil biology; nutrient cycling; breaking the cycles of pests, diseases, and weeds; productivity gains; and increased profitability. Thus, the challenge lies in the diffusion of production systems aligned with the principles of the no-tillage system and conservation agriculture, that is, to diversify without failing to produce and obtain gains from grain production. Information on the benefits of grain production systems that employ crop rotation with species diversification, tested and with demonstrated economicity, such as those presented in this study, can therefore be decisive for producers’ decision-making and the adoption of practices aligned with sustainability in agriculture. More

Mahmoodi, S. et al. The current and future potential geographical distribution of Nepeta crispa Willd., an endemic, rare and threatened aromatic plant of Iran: Implications for ecological conservation and restoration. Ecol. Indic. 137, 108752 (2022).
Google Scholar 
Behroozian, M., Ejtehadi, H., Peterson, A. T., Memariani, F. & Mesdaghi, M. Climate change influences on the potential distribution of Dianthus polylepis Bien. ex Boiss.(Caryophyllaceae), an endemic species in the Irano-Turanian region. PLoS ONE 15, e0237527 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Khanal, S. et al. Potential impact of climate change on the distribution and conservation status of Pterocarpus marsupium, a Near Threatened South Asian medicinal tree species. Ecol. Inform. 70, 101722 (2022).
Google Scholar 
Dyderski, M. K., Paź, S., Frelich, L. E. & Jagodziński, A. M. How much does climate change threaten European forest tree species distributions?. Glob. Change Biol. 24, 1150–1163 (2018).ADS 

Google Scholar 
Sanjerehei, M. M. & Rundel, P. W. The impact of climate change on habitat suitability for Artemisia sieberi and Artemisia aucheri (Asteraceae)—A modeling approach. Pol. J. Ecol. 65, 97–109 (2017).
Google Scholar 
Erfanian, M. B., Sagharyan, M., Memariani, F. & Ejtehadi, H. Predicting range shifts of three endangered endemic plants of the Khorassan-Kopet Dagh floristic province under global change. Sci. Rep. 11, 1–13 (2021).
Google Scholar 
Zhang, J. M. et al. Effects of climate change on the distribution of wild Akebia trifoliata. Ecol. Evol. 12, e8714 (2022).PubMed 
PubMed Central 

Google Scholar 
Li, J., Fan, G. & He, Y. Predicting the current and future distribution of three Coptis herbs in China under climate change conditions, using the MaxEnt model and chemical analysis. Sci. Total Environ. 698, 134141 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Yang, X.-Q., Kushwaha, S., Saran, S., Xu, J. & Roy, P. Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills. Ecol. Eng. 51, 83–87 (2013).CAS 

Google Scholar 
Greiser, C., Hylander, K., Meineri, E., Luoto, M. & Ehrlén, J. Climate limitation at the cold edge: Contrasting perspectives from species distribution modelling and a transplant experiment. Ecography 43, 637–647 (2020).
Google Scholar 
Guisan, A. & Thuiller, W. Predicting species distribution: Offering more than simple habitat models. Ecol. Lett. 8, 993–1009 (2005).PubMed 

Google Scholar 
Thuiller, W. et al. Predicting global change impacts on plant species’ distributions: Future challenges. Plant Ecol. Evol. Syst. 9, 137–152 (2008).
Google Scholar 
Menke, S., Holway, D., Fisher, R. & Jetz, W. Characterizing and predicting species distributions across environments and scales: Argentine ant occurrences in the eye of the beholder. Glob. Ecol. Biogeogr. 18, 50–63 (2009).
Google Scholar 
Warren, D. L. & Seifert, S. N. Ecological niche modeling in Maxent: The importance of model complexity and the performance of model selection criteria. Ecol. Appl. 21, 335–342 (2011).PubMed 

Google Scholar 
Celenk, S., Dirmenci, T., Malyer, H. & Bicakci, A. A palynological study of the genus Nepeta L.(Lamiaceae). Plant Syst. Evol. 276, 105–123 (2008).
Google Scholar 
Zargari, A. Medicinal Plants Vol. 2 (University of Tehran Pub, 1990).
Google Scholar 
Javidnia, K., Miri, R., Rezazadeh, S. R., Soltani, M. & Khosravi, A. R. Essential oil composition of two subspecies of Nepeta glomerulosa Boiss. from Iran. Nat. Prod. Commun. 3, 1934578X0800300530 (2008).
Google Scholar 
Jamzad, Z. Flora of Iran, no 76, Lamiaceae. Res. Inst. For. Rangel. Tehran 76, 542–544 (2012).
Google Scholar 
Talebi, S. M., Nohooji, M. G., Yarmohammadi, M., Azizi, N. & Matsyura, A. Trichomes morphology and density analysis in some Nepeta species of Iran. Mediterr. Bot. 39, 51–62 (2018).
Google Scholar 
Amirmohammadi, F., Azizi, M., Nemati, S. H., Memariani, F. & Murphy, R. Nutlet micro‐morphology of selected species of Nepeta (Lamiaceae) in Iran. Nord. J. Bot. (2019).Jamzad, Z., Chase, M. W., Ingrouille, M., Simmonds, M. S. & Jalili, A. Phylogenetic relationships in Nepeta L.(Lamiaceae) and related genera based on ITS sequence data. Taxon 52, 21–32 (2003).
Google Scholar 
Emami, S. A., Yazdian, R., Arab, A., Sadeghi, M. & Tayarani-Najaran, Z. Anti-melanogenic activity of different extracts from aerial parts of Nepeta glomeruloasin on murine melanoma B16F10 cells. Iran. J. Pharm. Sci. 13, 61–74 (2017).
Google Scholar 
Narimani, R., Moghaddam, M., Ghasemi Pirbalouti, A. & Mojarab, S. Essential oil composition of seven populations belonging to two Nepeta species from Northwestern Iran. Int. J. Food Prop. 20, 2272–2279 (2017).CAS 

Google Scholar 
Hosseini, A., Forouzanfar, F. & Rakhshandeh, H. Hypnotic effect of Nepeta glomerulosa on pentobarbital-induced sleep in mice. Jundishapur J. Nat. Pharm. Prod. https://doi.org/10.17795/jjnpp-25063 (2016).Article 

Google Scholar 
Layeghhaghighi, M., Hassanpour Asil, M., Abbaszadeh, B., Sefidkon, F. & Matinizadeh, M. Investigation of altitude on morphological traits and essential oil composition of Nepeta pogonosperma Jamzad and Assadi from Alamut region. J. Med. Plants Prod. 6, 35–40 (2017).
Google Scholar 
Sefidkon, F. Essential oil of Nepeta glomerulosa Boiss. from Iran. J. Essent. Oil Res. 13, 422–423 (2001).CAS 

Google Scholar 
Djamali, M. et al. Application of the global bioclimatic classification to Iran: Implications for understanding the modern vegetation and biogeography. Ecol. Mediterr. 37, 91–114 (2011).
Google Scholar 
Djamali, M., Brewer, S., Breckle, S. W. & Jackson, S. T. Climatic determinism in phytogeographic regionalization: a test from the Irano-Turanian region, SW and Central Asia. Flora Morphol. Distrib. Funct. Ecol. Plants 207, 237–249 (2012).
Google Scholar 
Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545 (2015).
Google Scholar 
Escobar, L. E., Lira-Noriega, A., Medina-Vogel, G. & Peterson, A. T. Potential for spread of the white-nose fungus (Pseudogymnoascus destructans) in the Americas: Use of Maxent and NicheA to assure strict model transference. Geospat. Health 9, 221–229 (2014).PubMed 

Google Scholar 
Valencia-Rodríguez, D., Jiménez-Segura, L., Rogéliz, C. A. & Parra, J. L. Ecological niche modeling as an effective tool to predict the distribution of freshwater organisms: The case of the Sabaleta Brycon henni (Eigenmann, 1913). PLoS ONE 16, e0247876 (2021).PubMed 
PubMed Central 

Google Scholar 
Merow, C., Smith, M. J. & Silander, J. A. Jr. A practical guide to MaxEnt for modeling species’ distributions: What it does, and why inputs and settings matter. Ecography 36, 1058–1069 (2013).
Google Scholar 
Peterson, A. T., Cobos, M. E. & Jiménez-García, D. Major challenges for correlational ecological niche model projections to future climate conditions. Ann. N. Y. Acad. Sci. 1429, 66–77 (2018).ADS 
PubMed 

Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
Google Scholar 
Raghavan, R. K., Peterson, A. T., Cobos, M. E., Ganta, R. & Foley, D. Current and future distribution of the lone star tick, Amblyomma americanum (L.)(Acari: Ixodidae) in North America. PLoS ONE 14, e0209082 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Muscarella, R. et al. ENM eval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods Ecol. Evol. 5, 1198–1205 (2014).
Google Scholar 
Ramírez Villegas, J. & Jarvis, A. Downscaling global circulation model outputs: The delta method decision and policy analysis Working Paper No. 1 (2010).Liu, C., Newell, G. & White, M. On the selection of thresholds for predicting species occurrence with presence-only data. Ecol. Evol. 6, 337–348 (2016).PubMed 

Google Scholar 
Austin, M. Species distribution models and ecological theory: A critical assessment and some possible new approaches. Ecol. Model. 200, 1–19 (2007).
Google Scholar 
Rahmanian, S., Pouyan, S., Karami, S. & Pourghasemi, H. R. In Computers in Earth and Environmental Sciences 245–254 (Elsevier, 2022).Rahmanian, S., Pourghasemi, H. R., Pouyan, S. & Karami, S. Habitat potential modelling and mapping of Teucrium polium using machine learning techniques. Environ. Monit. Assess. 193, 1–21 (2021).
Google Scholar 
Domroes, M., Kaviani, M. & Schaefer, D. An analysis of regional and intra-annual precipitation variability over Iran using multivariate statistical methods. Theor. Appl. Climatol. 61, 151–159 (1998).ADS 

Google Scholar 
Prevéy, J. et al. Greater temperature sensitivity of plant phenology at colder sites: Implications for convergence across northern latitudes. Glob. Change Biol. 23, 2660–2671 (2017).ADS 

Google Scholar 
Rousta, I. et al. Impacts of drought on vegetation assessed by vegetation indices and meteorological factors in Afghanistan. Remote Sens. 12, 2433 (2020).ADS 

Google Scholar 
Wang, Y. et al. Contrasting effects of temperature and precipitation on vegetation greenness along elevation gradients of the Tibetan Plateau. Remote Sens. 12, 2751 (2020).ADS 

Google Scholar 
Zhang, Y. et al. Vegetation change and its relationship with climate factors and elevation on the Tibetan plateau. Int. J. Environ. Res. Public Health 16, 4709 (2019).PubMed Central 

Google Scholar 
Vanneste, T. et al. Impact of climate change on alpine vegetation of mountain summits in Norway. Ecol. Res. 32, 579–593 (2017).
Google Scholar 
Rodriguez, C., Navarro, T. & El-Keblawy, A. Covariation in diaspore mass and dispersal patterns in three Mediterranean coastal dunes in southern Spain. Turk. J. Bot. 41, 161–170 (2017).
Google Scholar 
Zona, S. Fruit and seed dispersal of Salvia L.(Lamiaceae): A review of the evidence. Bot. Rev. 83, 195–212 (2017).
Google Scholar 
Ryding, O. Myxocarpy in the Nepetoideae (Lamiaceae) with notes on myxodiaspory in general. Syst. Geogr. Plants 71, 503–514 (2001).
Google Scholar 
Tanaka, K., Ogata, K., Mukai, H., Yamawo, A. & Tokuda, M. Adaptive advantage of myrmecochory in the ant-dispersed herb Lamium amplexicaule (Lamiaceae): Predation avoidance through the deterrence of post-dispersal seed predators. PLoS ONE 10, e0133677 (2015).PubMed 
PubMed Central 

Google Scholar 
Ferreira, P. M. et al. Long-term ecological research in southern Brazil grasslands: Effects of grazing exclusion and deferred grazing on plant and arthropod communities. PLoS ONE 15, e0227706 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar  More

Gorb, E. V. & Gorb, S. N. Anti-adhesive effects of plant wax coverage on insect attachment. J. Exp. Bot. 68, 5323–5337 (2017).CAS 
PubMed 

Google Scholar 
Agrawal, A. A. & Konno, K. Latex: A model for understanding mechanisms, ecology, and evolution of plant defense against herbivory. Annu. Rev. Ecol. Evol. Syst. 40, 311–331 (2009).
Google Scholar 
Langenheim, J. H. Plant resins. Am. Sci. 78, 16–24 (1990).
Google Scholar 
Ben-Mahmoud, S. et al. Acylsugar amount and fatty acid profile differentially suppress oviposition by western flower thrips, Frankliniella occidentalis, on tomato and interspecific hybrid flowers. PLoS ONE 13, 1–20 (2018).
Google Scholar 
LoPresti, E. F., Pearse, I. S. & Charles, G. K. The siren song of a sticky plant: Columbines provision mutualist arthropods by attracting and killing passerby insects. Ecology 96, 2862–2869 (2015).CAS 
PubMed 

Google Scholar 
Weinhold, A. & Baldwin, I. T. Trichome-derived O-acyl sugars are a first meal for caterpillars that tags them for predation. Proc. Natl. Acad. Sci. 108, 7855–7859 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Krimmel, B. A. & Wheeler, A. G. Host-plant stickiness disrupts novel ant–mealybug association. Arthropod. Plant. Interact. 9, 187–195 (2015).
Google Scholar 
Simmons, A. T., Gurr, G. M., McGrath, D., Martin, P. M. & Nicol, H. I. Entrapment of Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) on glandular trichomes of Lycopersicon species. Aust. J. Entomol. 43, 196–200 (2004).
Google Scholar 
Carter, C. D., Gianfagna, T. J. & Sacalis, J. N. Sesquiterpenes in glandular trichomes of a wild tomato species and toxicity to the colorado potato beetle. J. Agric. Food Chem. 37, 1425–1428 (1989).CAS 

Google Scholar 
Van Dam, N. M. & Hare, J. D. Biological activity of Datura wrightii glandular trichome exudate against Manduca sexta larvae. J. Chem. Ecol. 24, 1529–1549 (1998).
Google Scholar 
Kessler, A. & Heil, M. The multiple faces of indirect defences and their agents of natural selection. Funct. Ecol. 25, 348–357 (2011).
Google Scholar 
Karban, R., LoPresti, E., Pepi, A. & Grof-Tisza, P. Induction of the sticky plant defense syndrome in wild tobacco. Ecology 100, 1–9 (2019).
Google Scholar 
Krimmel, B. A. & Pearse, I. S. Sticky plant traps insects to enhance indirect defence. Ecol. Lett. 16, 219–224 (2013).CAS 
PubMed 

Google Scholar 
Eisner, T. & Aneshansley, D. J. Adhesive strength of the insect-trapping glue of a plant (Befaria racemosa). Ann. Entomol. Soc. Am. 76, 295–298 (1983).
Google Scholar 
Spomer, G. G. Evidence of protocarnivorous capabilities in Geranium viscosissimum and Potentilla arguta and other sticky plants. Int. J. Plant Sci. 160, 98–101 (1999).
Google Scholar 
Darnowski, D. W., Carroll, D. M., Płachno, B., Kabanoff, E. & Cinnamon, E. Evidence of protocarnivory in triggerplants (Stylidium spp.; Stylidiaceae). Plant Biol. 8, 805–812 (2006).CAS 
PubMed 

Google Scholar 
Givnish, T. J., Burkhardt, E. L., Happel, R. E. & Weintraub, J. D. Carnivory in the bromeliad Brocchinia reducta, with a cost/benefit model for the general restriction of carnivorous plants to sunny, moist nutrient-poor habitats. Am. Nat. 124, 479–497 (1984).
Google Scholar 
Jürgens, N. Psammophorous plants and other adaptations to desert ecosystems with high incidence of sandstorms. Feddes Repert. 107, 345–359 (1996).
Google Scholar 
Lopresti, E. F. & Karban, R. Chewing sandpaper: Grit, plant apparency, and plant defense in sand-entrapping plants. Ecology 97, 826–833 (2016).PubMed 

Google Scholar 
Krupnick, G. A. & Weis, A. E. The effect of floral herbivory on male and female reproductive success in Isomeris arborea. Ecology 80, 135–149 (1999).
Google Scholar 
McCall, A. C. Florivory affects pollinator visitation and female fitness in Nemophila menziesii. Oecologia 155, 729–737 (2008).ADS 
PubMed 

Google Scholar 
Bandeili, B. & Müller, C. Folivory versus florivory-adaptiveness of flower feeding. Naturwissenschaften 97, 79–88 (2010).ADS 
CAS 
PubMed 

Google Scholar 
Lai, D. et al. Lotus japonicus flowers are defended by a cyanogenic β-glucosidase with highly restricted expression to essential reproductive organs. Plant Mol. Biol. 89, 21–34 (2015).CAS 
PubMed 

Google Scholar 
Kessler, A. & Halitschke, R. Testing the potential for conflicting selection on floral chemical traits by pollinators and herbivores: Predictions and case study. Funct. Ecol. 23, 901–912 (2009).
Google Scholar 
Kessler, D., Diezel, C., Clark, D. G., Colquhoun, T. A. & Baldwin, I. T. Petunia flowers solve the defence/apparency dilemma of pollinator attraction by deploying complex floral blends. Ecol. Lett. 16, 299–306 (2013).PubMed 

Google Scholar 
Li, J. et al. Defense of pyrethrum flowers: Repelling herbivores and recruiting carnivores by producing aphid alarm pheromone. New Phytol. 223, 1607–1620 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kennedy, G. G. Tomato, pests, parasitoids, and predators: tritrophic interactions involving the genus Lycopersicon. Annu. Rev. Entomol. 48, 51–72 (2003).CAS 
PubMed 

Google Scholar 
McCarren, S., Coetzee, A. & Midgley, J. Corolla stickiness prevents nectar robbing in Erica. J. Plant Res. https://doi.org/10.1007/s10265-021-01299-z (2021).Article 
PubMed 

Google Scholar 
Matulevich Peláez, J. A., Gil Archila, E. & Ospina Giraldo, L. F. Estudio fitoquímico de hojas, flores y frutos de Bejaria resinosa mutis ex linné filius (ericaceae) y evaluación de su actividad antiinflamatoria. Rev. Cuba. Plantas Med. 21, 332–345 (2016).
Google Scholar 
Kraemer, M. On the pollination of Bejaria resinosa Mutis ex Linne f. ( Ericaceae ), an ornithophilous Andean paramo shrub. Flora 196, 59–62 (2001).
Google Scholar 
Melampy, A. M. N. Flowering phenology, pollen flow and fruit production in the Andean Shrub Befaria resinosa. Oecologia 73, 293–300 (1987).ADS 
CAS 
PubMed 

Google Scholar 
LoPresti, E. F., Robinson, M. L., Krimmel, B. A. & Charles, G. K. The sticky fruit of manzanita: potential functions beyond epizoochory. Ecology 99, 2128–2130 (2018).PubMed 

Google Scholar 
Kessler, A. & Chautá, A. The ecological consequences of herbivore-induced plant responses on plant-pollinator interactions. Emerg. Topics Life Sci. 4, 33–43 (2020).
Google Scholar 
Lucas-Barbosa, D. Integrating studies on plant-pollinator and plant-herbivore interactions. Trends Plant Sci. 21, 125–133 (2016).CAS 
PubMed 

Google Scholar 
Leckie, B. M. et al. Differential and synergistic functionality of acylsugars in suppressing oviposition by insect herbivores. PLoS ONE 11, 1–19 (2016).
Google Scholar 
Monteiro, R. F. & Macedo, M. V. First report on the diversity of insects trapped by a sticky exudate of the inflorescences of Vriesea bituminosa Wawra (Bromeliaceae: Tillandsioideae). Arthropod. Plant. Interact. 8, 519–523 (2014).
Google Scholar 
Chatzivasileiadis, E. A. & Sabelis, M. W. Toxicity of methyl ketones from tomato trichomes to Tetranychus urticae Koch. Exp. Appl. Acarol. 21, 473–484 (1997).CAS 

Google Scholar 
Avé, D. A., Gregory, P. & Tingey, W. M. Aphid repellent sesquiterpenes in glandular trichomes of Solanum berthaultii and S. tuberosum. Entomol. Exp. Appl. 44, 131–138 (1987).
Google Scholar 
LoPresti, E. Columbine pollination success not determined by a proteinaceous reward to hummingbird pollinators. J. Pollinat. Ecol. 20, 35–39 (2017).
Google Scholar 
Krimmel, B. A. & Pearse, I. S. Generalist and sticky plant specialist predators suppress herbivores on a sticky plant. Arthropod. Plant. Interact. 8, 403–410 (2014).
Google Scholar 
Adlassnig, W., Lendl, T., Peroutka, M. & Lang, I. Deadly glue- Adhesive traps of carnivorous plants. in Biological Adhesive Systems (eds. von Byren, J. & Grunwald, I.) 15–28 (2010).Ellison, A. M. & Gotelli, N. J. Evolutionary ecology of carnivorous plants. Trends Ecol. Evol. 16, 623–629 (2001).
Google Scholar 
Maloof, J. E. & Inouye, D. W. Are nectar robbers cheaters or mutualists?. Ecology 81, 2651–2661 (2000).
Google Scholar 
Asai, T., Hirayama, Y. & Fujimoto, Y. Epi-α-bisabolol 6-deoxy-β-d-gulopyranoside from the glandular trichome exudate of Brillantaisia owariensis. Phytochem. Lett. 5, 376–378 (2012).CAS 

Google Scholar 
Asai, T., Hara, N. & Fujimoto, Y. Fatty acid derivatives and dammarane triterpenes from the glandular trichome exudates of Ibicella lutea and Proboscidea louisiana. Phytochemistry 71, 877–894 (2010).CAS 
PubMed 

Google Scholar 
Ohkawa, A., Sakai, T., Ohyama, K. & Fujimoto, Y. Malonylated glycerolipids from the glandular trichome exudate of Ceratotheca triloba. Chem. Biodivers. 9, 1611–1617 (2012).CAS 
PubMed 

Google Scholar 
Omosa, L. K. et al. Antimicrobial flavonoids and diterpenoids from Dodonaea angustifolia. S. Afr. J. Bot. 91, 58–62 (2014).CAS 

Google Scholar 
Kessler, A. The information landscape of plant constitutive and induced secondary metabolite production. Curr. Opin. Insect Sci. 8, 47–53 (2015).PubMed 

Google Scholar 
Knudsen, J. T., Tollsten, L., Groth, I., Bergström, G. & Raguso, R. A. Trends in floral scent chemistry in pollination syndromes: Floral scent composition in hummingbird-pollinated taxa. Bot. J. Linn. Soc. 146, 191–199 (2004).
Google Scholar 
Pearse, I. S., Gee, W. S. & Beck, J. J. Headspace volatiles from 52 oak species advertise induction, species identity, and evolution, but not defense. J. Chem. Ecol. 39, 90–100 (2013).CAS 
PubMed 

Google Scholar 
El-Sayed, A. M., Byers, J. A. & Suckling, D. M. Pollinator-prey conflicts in carnivorous plants: When flower and trap properties mean life or death. Sci. Rep. 6, 1–11 (2016).
Google Scholar 
Greenaway, W., May, J. & Whatley, F. R. Analysis of phenolics of bud exudate of Populus tristis by GC/MS. Zeitschrift fur Naturforsch.. Sect C J. Biosci. 47, 512–515 (1992).
Google Scholar 
Urzua, A. & Cuadra, P. Acylated flavonoid aglycones from Gnaphalium robustum. Phytochem. Divers. Redundancy Ecol. Interact. 29, 1342–1343 (1990).CAS 

Google Scholar 
Drewes, S. E., Mudau, K. E., Van Vuuren, S. F. & Viljoen, A. M. Antimicrobial monomeric and dimeric diterpenes from the leaves of Helichrysum tenax var tenax. Phytochemistry 67, 716–722 (2006).CAS 
PubMed 

Google Scholar 
Midiwo, J. O. et al. Bioactive compounds from some Kenyan ethnomedicinal plants: Myrsinaceae, Polygonaceae and Psiadia punctulata. Phytochem. Rev. 1, 311–323 (2002).CAS 

Google Scholar 
Jiménez-Pomárico, A. et al. Chemical and morpho-functional aspects of the interaction between a Neotropical resin bug and a sticky plant. Rev. Biol. Trop. 67, 454–465 (2019).
Google Scholar 
Linhart, Y. B., Thompson, J. D., Url, S. & John, D. Terpene-based selective herbivory by Helix aspersa (Mollusca) on Thymus vulgaris (Labiatae). Oecologia 102, 126–132 (2012).
Google Scholar 
Kessler, A., Halitschke, R. & Poveda, K. Herbivory-mediated pollinator limitation: Negative impacts of induced volatiles on plant-pollinator interactions. Ecology 92, 1769–1780 (2011).PubMed 

Google Scholar 
Sletvold, N., Moritz, K. K. & Ågren, J. Additive effects of pollinators and herbivores result in both conflicting and reinforcing selection on floral traits. Ecology 96, 214–221 (2015).PubMed 

Google Scholar 
Ramos, S. E. & Schiestl, F. P. Rapid plant evolution driven by the interaction of pollination and herbivory. Science (80-). 364, 193–196 (2019).ADS 
CAS 

Google Scholar 
Rojas-Nossa, S. V. Estrategias de extracción de néctar por pinchaflores (Aves: Diglossa y Diglossopis) y sus efectos sobre la polinización de plantas de los altos Andes. Ornitol. Colomb. 5, 21–39 (2007).
Google Scholar 
R Team Core. R: A language and environment for statistical computing. R Foundation for Statistical Computing. (2021).Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).
Google Scholar 
Diaz-Uriarte, R. Package ‘ varSelRF ’. Compr. R Arch. Netw. 1–23 (2015). More