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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

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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

According to the target of the present study, the mortality incidence on P. nobilis in local populations along the Apulia peninsula (the Southeast coast of Italy) following the MME was assessed. In addition, an investigation on the species distribution and densities in the Adriatic and the Ionian Sea was carried out, which allowed us to build a picture of species populations before the MME.Concerning the P. nobilis distribution in the Apulia region before the MME, unfortunately, there is a lack of information at the wide scale, and literature reports only concern semi-enclosed systems such as the Taranto basins17,18,19 and the Aquatina lagoon20. No large-scale monitoring program on P. nobilis, in fact, has been carried out previously along the Apulian coast, although this kind of surveys is indispensable for the management of a protected species and must become mandatory for a critically endangered species such has become P. nobilis. The present data-gathering, that is aimed to partially address this information gap, based on the monitoring of recently dead specimens, allowed to realize a plausible map of P. nobilis distribution and densities before the MME in 30 areas distributed along the entire Apulian region coast.Along the Ionian coast, recently dead P. nobilis were detected in all the areas studied, highlighting a continuous distribution of the species prior to the MME, differently from the not continuous distribution along the Adriatic coast. The occurrence of P. nobilis was recorded in the areas surveyed in the south, from A7 to A17, but no traces were found along the northernmost areas except for the Tremiti archipelago, suggesting that the northernmost Adriatic coast of the region does not meet the environmental conditions suitable for hosting this species. Nevertheless, in the Gulf of Manfredonia multiple reports from fisherman indicating the presence of the species in a local Cymodocea nodosa meadow before the 1980s, suggest that this area may have been an exception in the past. Therefore, we can assume that excessive fishing and anthropogenic activities in this area are likely to have caused the species to disappear many decades ago.Data regarding the mortality incidence after the MME in Apulian populations is scarce. Panarese et al.11 reported the advent of the disease in Mar Piccolo di Taranto but without describing the disease incidence. In this study, a mortality incidence of 100% in all basins, bathymetric (down to 15 m) and habitat types, was recorded, demonstrating the severity of the situation along the entire Apulian coast, both inshore and offshore, and in lagoon and marine-protected areas.Although the availability of nutrients and the trophic conditions are assumed to be very different between offshore, inshore, and transitional systems, the archipelago of Tremiti islands, located 13 miles away from the coast, showed no differences in mortality incidence from sites along the coast, evidencing the same critical conditions in all environments.Many Mediterranean lagoon systems, including the Ebro Delta, Mar Menor Lagoon in Spain21, the Rhone delta, Leucate and Thau in France22,23,24, Venice, Grado-Marano and Faro in Italy25,26,27, Bizerte in Tunisia24 are considered the last healthy shelters for P. nobilis populations in the Mediterranean Sea22. These systems seem to offer a degree of resistance against the disease and are all characterized by high seasonal fluctuations of environmental parameters, such as temperature and salinity. It has been supposed that the effect of these fluctuations could make these environments less suitable for the spread of the disease and reduce the rate of transmission21,22. In the present study, two lagoon systems were also investigated, but no live specimens were found. These systems are strongly affected by the saltwater intrusion and the freshwater inputs became very low during the dry season. Hence, we can assume that during the summer season, when P. nobilis become susceptible to the disease, no salinity barrier against the pathogen spread persists in these lagoons systems.Considering that the lagoon refuges currently represent the main source of larval production for P. nobilis recruitment22,28, the collapse of these populations confirms the severity of the situation for species conservation. For the Italian coast, the last live populations are those in the lagoons located in the northen Adriatic Sea (Venice and Grado-Marano lagoon). These environments can act as larval exporters for the Adriatic Sea taking advantage of the mobility of the larvae that can spread over hundreds of kms28.Regarding the timeframe of the spread of the MME along the Apulian coast, the first report of the infection dates back to 201818, in the Mar Piccolo di Taranto. Compared to the first MME event observed in the Spanish coast in 20165,7, the disease has spread from the western to the eastern basin of the Mediterranean Sea over a period of 2 years. Our surveys, carried out in 2020, showed that 91% of the shells were still undamaged and with joined valves. Based on the state of conservation of the shells29 it is possible to hypothesize that the death of the specimens was a recent phenomenon that had occurred in Apulia in the two years preceding our surveys, and most probably it should be dated back to 2019.Kersting and Ballesteros30 have suggested that other species, such as P. rudis, could benefit from the collapse of the P. nobilis population. During our surveys, only 5 specimens of P. rudis were found, located in 2 sites, but it must be considered that the survey was carried out only a short time after the MME of P. nobilis. Further studies aimed at assessing an increase in P. rudis in the investigated areas would be of great interest to corroborate this hypothesis.In these surveys, P. nobilis showed transverse distribution among habitat types occurring both in marine and lagoon systems, inside and outside seagrass meadows, on sandy, rocky, and maerl beds substrate. Nevertheless, on a spatial macro (from a few kilometers to tens of kilometers) and mesoscale (from hundreds to thousands of meters), an overlap with the distributional range of seagrass meadows emerges. A clear cross-boundary subsidy trend was evidenced by the data collected on P. nobilis distribution in association with seagrasses. The specimens inside seagrass meadows were almost double than those detected nearby and a gradual decrease was observed with the increase of the distance from the seagrass patches (Fig. 2). This is particularly evident along the northern Adriatic coast of the region, where extended seagrass meadows are absent and, no trace of P. nobilis was encountered, except in the Tremiti archipelago where both P. oceanica meadows and pen shells were found. By contrast, present data reporting P. nobilis as associated with various seagrass species, such as P. oceanica, C. nodosa, and Zostera sp., are consistent with the macroscale and mesoscale association between P. nobilis and seagrass meadows sensu lato and most literature reporting ubiquitous distribution of P. nobilis both in lagoon-estuarine21,22,24,25,26,31 and in marine ecosystems4,7,9,14,16,24.However, regarding their microscale distribution, the pen shells in our surveys were recorded also outside the seagrass meadows boundaries, at times up to 1 km away. Hence, seagrass sheltering can potentially be ruled out as the sole explanatory factor for the distribution pattern of the species. The pattern emerging from this study led us to hypothesize that a trophic link with the seagrass detritus food-chain may explain both the macroscale–mesoscale association with seagrass species and the microscale cross-boundary distribution. In fact, seagrass detritus is highly refractory, since it is largely exported to the nearby areas where it can represent the major food source for other invertebrates32,33,34. This hypothesis is consistent with the stomach contents observations reported by Davenport et al.3 indicating detritus as the bulk component, accounting for 95% of the total ingested material.One of the main factors underlying the distribution pattern in benthic invertebrates is indeed food availability35,36. According to the Ideal Free Distribution (IFD) theory, the individuals in a population disperse to different resource patches within their environment, minimizing competition and maximizing fitness37. When the IFD assumptions are met, the number of individuals who aggregate in patches is proportional to the amount of food resource available in each one. Accordingly, the distribution of large, long life, and sessile organisms such as P. nobilis would be expected to depict the species trophic supply, by analyzing the resources available in those patches.Studies on the seagrass system energy flow have shown that seagrass debris must be fractionated before entering the food chain33. In this way, plant material becomes fine particulates moving in the boundary layer over the sediment–water interface38,39. These processes take time, and while the matter is transported, heterotrophic bacteria grow exponentially, turning it into a high quality and protein-enriched food for consumers. Hence, bacteria adhering to seagrass detritus may play a key role in this benthic food chain and sediment–water interface consumers may incorporate more energy from associated microbes than from the detritus itself32,38. On the basis of these considerations, it is reasonable to hypothesize that the quantity, composition and origin of the suspended particles are regulated by a drift mechanism and that this mechanism may explain local densities of P. nobilis as a response to sinking rates and resuspension effects. This hypothesis explain also the species distribution in systems, characterized by strong dominant current and shallow seabeds where the seagrass detritus can be spread/drift several kilometers away from the meadows. An example of this condition is encountered in the north Adriatic Sea (e.g., Gulf of Trieste) where extensive population of P. nobilis develops on several sink areas even kilometers downstream from the meadows. The assumption of the species’ ability to feed on seagrass detritus, together with the high biomasses reached (large size specimens and high density), lead us to suppose that P. nobilis may play a key role in the processing of matter and in the energy pathway deriving from seagrass detritus in Mediterranean coastal areas. This makes the repercussions of the MME not only a problem of conservation, but also and above all, an ecological-functional issue.We can, therefore, conclude that Mediterranean seagrass meadows not only constitute a habitat for P. nobilis, but probably also a food source through refractory detritus generation which is transferred and transformed outside the meadows. Unfortunately, literature is lacking on this topic and further investigations are needed to define the trophic role and function of these filter feeders in the different seagrass meadows.The density values that emerged were significantly different among basins. In the Adriatic Sea, where all the coastal values were recorded, the densities were consistently lower than those reported in the Ionian Sea, except for the two southernmost areas. In the Adriatic basin, it was also possible to recognize a north-south trend when considering the densities of pen shells in the coastal areas. Although the values recorded along the southern coast of the region were much greater than those recorded in the central coast, they were far lower than those reported by Čižmek et al.40 in the Croatian coast (North Adriatic Sea). Similar values to ours within the same basin were reported by Celebicic et al.41 in Bosnian waters (0.12 individuals/100 m2).On the other hand, in the Ionian areas, the values recorded were consistently >0.1 individuals/100 m2. The values recorded in the Mar Grande di Taranto were higher than those reported by Centoducati et al.17 (0.1–0.7 ind/ha2). From interviews with fishermen, it emerged that illegal trawling in this area has strongly impacted the natural populations of the Mar Grande di Taranto, and a partial reduction of this activity, in recent years could explain the slight increase in density compared to the 2004 survey data17.In interpreting our data, it should be considered that the surveys were carried out employing an extensive sampling protocol conceived to assess wide surface densities on coastal areas investigating across several habitat types. Therefore, literature density values focused only on local areas or habitat patchiness that were not randomly selected must be contextualized when compared with these data. In addition, given the scale of the presented surveys, emphasis must be given to P. nobilis absence data of which the literature appears poor. Indeed, contrary to the data on presence, reliable absence data are difficult to obtain requiring much greater effort to rule out a rare occurrence42. The absence data obtained in this study derive from the merger of two different data types. The first come from the local ecological knowledge obtained from interviews with the local fishermen, which allowed us to confirm our data, excluding spot occurrences in the same areas. Furthermore the interviews allowed us to collect information on a historical series of species presence/absence in the areas, which was helpful to confirm local absence when no P. nobilis specimens were recorded in our surveys. The second derives from the complete vision of divers during the field surveys. Indeed the scuba diver’s view was at least 10 times wider than 50 cm from the side around the rope and hence, the perception of absence can be extended over a much larger surface area investigated. By merging these two sources of information, we can assume that the absence data collected in exhaustive and complete.In conclusion, this study investigated different basins, habitat types, and bathymetries along the Apulian coast. The shells spatial distribution that arise from this study allowed to obtain important information on the species trophic ecology. Indeed, the species distributional pattern showed a strong overlap with seagrass meadows on meso and macro geographical scale, however this was not the case on a micro scale. This result indicates that although there is a strong relationship between P. nobilis and seagrass meadows, it is not limited to the habitat patch but crosses the boundaries of seagrass. This result led us to hypothesize that the distribution of P. nobilis displays a trophic link through the cross-boundary subsidy occurring from seagrass meadows to the nearby habitat, by means of the refractory detrital pathway. However, further investigations taking into account other factors such as hydrodynamics, are needed to investigate this topic.No live specimens of P. nobilis were found in >800 km of coastal line, leading us to the conclusion that the coastal and lagoon population had totally collapsed in the region after the MME. The seriousness of the situation on the Apulian coasts, just as in the other Mediterranean ecoregions, indicates that the MME that began in 2016 is still in progress, and no local population can be considered safe. Given the gravity of the current situation, it is vital for species preservation to extend the survey across the entire Italian coast to gain a overall picture of the status of the P. nobilis population on a national scale. Indeed, other regions may reveal the existence of natural shelters, where live populations of P. nobilis may still persist. If this is the case, it is essential to identify and protect them in time. As already suggested by Kersting et al.9, this initiative should be conducted in parallel by all the nations of the Mediterranean basin to implement standard guidelines for the monitoring, protection, and recovery of this critically endangered species. More

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

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