Philippa Kaur

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

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

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

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

Hobbs, R. J. et al. Novel ecosystems: Theoretical and management aspects of the new ecological world order. Glob. Ecol. Biogeogr. 15, 1–7 (2006).
Google Scholar 
Kraft, N. J. B. et al. Community assembly, coexistence and the environmental filtering metaphor. Funct. Ecol. 29, 592–599 (2015).
Google Scholar 
Mayfield, M. M. et al. What does species richness tell us about functional trait diversity? Predictions and evidence for responses of species and functional trait diversity to land-use change. Glob. Ecol. Biogeogr. 19, 423–431 (2010).
Google Scholar 
Zobel, M. The species pool concept as a framework for studying patterns of plant diversity. J. Veg. Sci. 27, 8–18 (2016).
Google Scholar 
Birkhofer, K. et al. Land-use type and intensity differentially filter traits in above- and below-ground arthropod communities. J. Anim. Ecol. 86, 511–520 (2017).PubMed 

Google Scholar 
Temperton, V. M. Assembly Rules and Restoration Ecology: Bridging the Gap Between Theory and Practice (Island Press, 2004).
Google Scholar 
Flynn, D. F. B. et al. Loss of functional diversity under land use intensification across multiple taxa. Ecol. Lett. 12, 22–33 (2009).PubMed 

Google Scholar 
Gascon, C. et al. Matrix habitat and species richness in tropical forest remnants. Biol. Conserv. 91, 223–229 (1999).
Google Scholar 
Filloy, J., Zurita, G. A., Corbelli, J. M. & Bellocq, M. I. On the similarity among bird communities: Testing the influence of distance and land use. Acta Oecol. 36, 333–338 (2010).ADS 

Google Scholar 
Vaccaro, A., Filloy, J. & Bellocq, M. What land use better preserves the functional and taxonomic diversity of birds in a grassland biome?. Avian Conserv. Ecol. 14, 1 (2019).
Google Scholar 
Vaccaro, A. S. & Bellocq, M. I. Diversidad taxonómica y funcional de aves: Diferencias entre hábitats antrópicos en un bosque subtropical. Ecol. Austral 29, 391–404 (2019).
Google Scholar 
Sekercioglu, C. H. Bird functional diversity and ecosystem services in tropical forests, agroforests and agricultural areas. J. Ornithol. 153, 153–161 (2012).
Google Scholar 
Zurita, G. A. & Bellocq, M. I. Bird assemblages in anthropogenic habitats: Identifying a suitability gradient for native species in the Atlantic Forest. Biotropica 44, 412–419 (2012).
Google Scholar 
Azpiroz, A. B. et al. Ecology and conservation of grassland birds in southeastern South America: A review. J. Field Ornithol. 83, 217–246 (2012).
Google Scholar 
Devictor, V. et al. Spatial mismatch and congruence between taxonomic, phylogenetic and functional diversity: The need for integrative conservation strategies in a changing world. Ecol. Lett. 13, 1030–1040 (2010).PubMed 

Google Scholar 
Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Conserv. 61, 1–10 (1992).
Google Scholar 
Corbelli, J. M. et al. Integrating taxonomic, functional and phylogenetic beta diversities: Interactive effects with the biome and land use across taxa. PLoS ONE 10, 1–17 (2015).
Google Scholar 
Purschke, O. et al. Contrasting changes in taxonomic, phylogenetic and functional diversity during a long-term succession: Insights into assembly processes. J. Ecol. 101, 857–866 (2013).
Google Scholar 
Srivastava, D. S., Cadotte, M. W., Macdonald, A. A. M., Marushia, R. G. & Mirotchnick, N. Phylogenetic diversity and the functioning of ecosystems. Ecol. Lett. 15, 637–648 (2012).PubMed 

Google Scholar 
Cavender-Bares, J., Kozak, K. H., Fine, P. V. A. & Kembel, S. W. The merging of community ecology and phylogenetic biology. Ecol. Lett. 12, 693–715 (2009).PubMed 

Google Scholar 
Mouquet, N. et al. Ecophylogenetics: Advances and perspectives. Biol. Rev. 87, 769–785 (2012).PubMed 

Google Scholar 
Ackerly, D. D., Schwilk, D. W. & Webb, C. O. Niche evolution and adaptive radiation: Testing the order of trait divergence. Ecology 87, S50–S61 (2006).CAS 
PubMed 

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

Google Scholar 
Losos, J. B. et al. Niche lability in the evolution of a Caribbean lizard community. Nature 424, 542–545 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Stevens, R. D., Gavilanez, M. M., Tello, J. S. & Ray, D. A. Phylogenetic structure illuminates the mechanistic role of environmental heterogeneity in community organization. J. Anim. Ecol. 81, 455–462 (2012).PubMed 

Google Scholar 
García-Navas, V. & Thuiller, W. Farmland bird assemblages exhibit higher functional and phylogenetic diversity than forest assemblages in France. J. Biogeogr. 47, 2392–2404 (2020).
Google Scholar 
Henwood, W. D. Toward a strategy for the conservation and protection of the world’s temperate grasslands. Univ. Neb. Press 20, 121–134 (2010).ADS 

Google Scholar 
Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).ADS 
CAS 
PubMed 

Google Scholar 
Landi, M., Oesterheld, M. & Deregibus, V. A. Manual de especies forrajeras de los pastizales naturales de Entre Ríos (1987).Viglizzo, E. F. et al. Ecological lessons and applications from one century of low external-input farming in the pampas of Argentina. Agric. Ecosyst. Environ. 83, 65–81 (2001).
Google Scholar 
Galindo Leal, C. & de Gusmão Câmara, I. The Atlantic Forest of South America: Biodiversity Status, Threats and Outlook (Island Press, 2003).
Google Scholar 
Oliveira-Filho, A. T. & Fontes, M. A. L. Patterns of floristic differentiation among Atlantic Forests in Southeastern Brazil and the influence of climate. Biotropica 32, 793–810 (2000).
Google Scholar 
DeGraaf, R. M., Geis, A. D. & Healy, P. A. Bird population and habitat surveys in urban areas. Landsc. Urban Plan. 21, 181–188 (1991).
Google Scholar 
Ralph, C. J. et al. Manual de métodos de campo para el monitoreo de aves terrestres. Pacific Southwest Research Station, Forest Service, U.S. Department of Agriculture, Albany, CA 46 http://www.srs.fs.usda.gov/pubs/31462. https://doi.org/10.3145/epi.2006.jan.15 (1996).Bibby, C., Jones, M. & Marsden, S. Expedition field techniques: Bird surveys. in (ed. Society, R. G.) (1998).Zurita, G. A. & Bellocq, M. I. Spatial patterns of bird community similarity: Bird responses to landscape composition and configuration in the Atlantic forest. Landsc. Ecol. 25, 147–158 (2010).
Google Scholar 
Koper, N. & Schmiegelow, F. K. K. A multi-scaled analysis of avian response to habitat amount and fragmentation in the Canadian dry mixed-grass prairie. Landsc. Ecol. 21, 1045 (2006).
Google Scholar 
Xeno-canto-Foundation. Xeno-canto website. https://www.xeno-canto.org (2018).Petchey, O. L. & Gaston, K. J. Functional diversity (FD), species richness and community composition. Ecol. Lett. 5, 402–411 (2002).
Google Scholar 
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).ADS 
CAS 
PubMed 

Google Scholar 
Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.r-project.org (2018).Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).CAS 
PubMed 

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

Google Scholar 
Pinheiro, J., Bates, D., DebRoy, S. & Sarkar, D. R Core Team. nlme: Linear and Nonlinear mixed effects models. R package version 3.1–117. (2014).Lenth, R. V. Least-Squares Means: The R Package lsmeans. J. Stat. Softw. https://doi.org/10.18637/jss.v069.i01 (2016).Article 

Google Scholar 
Cadotte, M. W. & Tucker, C. M. Should environmental filtering be abandoned?. Trends Ecol. Evol. 32, 429–437 (2017).PubMed 

Google Scholar 
Concepción, E. D. et al. Contrasting trait assembly patterns in plant and bird communities along environmental and human-induced land-use gradients. Ecography 40, 753–763 (2016).
Google Scholar 
Cerezo, A., Conde, M. C. & Poggio, S. L. Pasture area and landscape heterogeneity are key determinants of bird diversity in intensively managed farmland. Biodivers. Conserv. 20, 2649–2667 (2011).
Google Scholar 
Pretelli, M. G., Isacch, J. P. & Cardoni, D. A. Year-round abundance, richness and nesting of the bird assemblage of tall grasslands in the south-east Pampas region, Argentina. Ardeola 60, 327–343 (2013).
Google Scholar 
Molinari, R. L. Biografía de la Pampa: 4 siglos de historia del campo argentino (Fundación Colombina “V Centenario,” 1987).
Google Scholar 
Filloy, J. & Bellocq, M. I. Patterns of bird abundance along the agricultural gradient of the Pampean region. Agric. Ecosyst. Environ. 120, 291–298 (2007).
Google Scholar 
Le Viol, I. et al. More and more generalists: Two decades of changes in the European avifauna. Biol. Lett. 8, 780–782 (2012).PubMed 
PubMed Central 

Google Scholar 
Concepción, E. D., Moretti, M., Altermatt, F., Nobis, M. P. & Obrist, M. K. Impacts of urbanisation on biodiversity: The role of species mobility, degree of specialisation and spatial scale. Oikos 124, 1571–1582 (2015).
Google Scholar 
Emerson, B. C. & Gillespie, R. G. Phylogenetic analysis of community assembly and structure over space and time. Trends Ecol. Evol. 23, 619–630 (2008).PubMed 

Google Scholar 
Morse, N. B. et al. Novel ecosystems in the Anthropocene: A revision of the novel ecosystem concept for pragmatic applications. Ecol. Soc. 19, 12 (2014).
Google Scholar 
Loyn, R. H., McNabb, E. G., Macak, P. & Noble, P. Eucalypt plantations as habitat for birds on previously cleared farmland in south-eastern Australia. Biol. Conserv. 137, 533–548 (2007).
Google Scholar 
Marsden, S., Whiffin, M. & Galetti, M. Bird diversity and abundance in forest fragments and Eucalyptus plantations around an Atlantic forest reserve, Brazil. Biodivers. Conserv. 10, 737–751 (2001).
Google Scholar 
Zurita, G. A., Rey, N., Varela, D. M., Villagra, M. & Bellocq, M. I. Conversion of the Atlantic Forest into native and exotic tree plantations: Effects on bird communities from the local and regional perspectives. For. Ecol. Manag. 235, 164–173 (2006).
Google Scholar 
Flynn, D. F. B., Mirotchnick, N., Jain, M., Palmer, M. I. & Naeem, S. Functional and phylogenetic diversity as predictors of biodiversity ecosystem-function. Ecology 92, 1573–1581 (2011).PubMed 

Google Scholar 
Sol, D. et al. The worldwide impact of urbanisation on avian functional diversity. Ecol. Lett. 23, 962–972 (2020).PubMed 

Google Scholar 
Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Syst. 33, 475–505 (2002).
Google Scholar 
Palacio, F. X., Ibañez, L. M., Maragliano, R. E. & Montalti, D. Urbanization as a driver of taxonomic, functional, and phylogenetic diversity losses in bird communities. Can. J. Zool. 96, 1114–1121 (2018).
Google Scholar 
Sol, D., Bartomeus, I., González-Lagos, C. & Pavoine, S. Urbanisation and the loss of phylogenetic diversity in birds. Ecol. Lett. 20, 721–729 (2017).PubMed 

Google Scholar 
Luck, G. W., Carter, A. & Smallbone, L. Changes in bird functional diversity across multiple land uses: Interpretations of functional redundancy depend on functional group identity. PLoS ONE 8, e63671 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Coetzee, B. W. T. & Chown, S. L. Land-use change promotes avian diversity at the expense of species with unique traits. Ecol. Evol. 6, 7610–7622 (2016).PubMed 
PubMed Central 

Google Scholar  More

These authors contributed equally: Laura H. Antão, Benjamin Weigel.These authors jointly supervised this work: Tomas Roslin, Anna-Liisa Laine.Research Centre for Ecological Change, Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, FinlandLaura H. Antão, Benjamin Weigel, Giovanni Strona, Maria Hällfors, Elina Kaarlejärvi, Otso Ovaskainen, Marjo Saastamoinen, Jarno Vanhatalo, Tomas Roslin & Anna-Liisa LaineDepartment of Biological Sciences, University of South Carolina, Columbia, SC, USATad DallasDepartment of Biology, Lund University, Lund, SwedenØystein H. OpedalFinnish Environment Institute (SYKE), Helsinki, FinlandJanne Heliölä, Mikko Kuussaari, Juha Pöyry & Kristiina VuorioNatural Resources Institute Finland (Luke), Helsinki, FinlandHeikki Henttonen, Otso Huitu, Andreas Lindén, Päivi Merilä, Maija Salemaa & Tiina TonteriSection of Ecology, Department of Biology, University of Turku, Turku, FinlandErkki KorpimäkiFinnish Museum of Natural History, University of Helsinki, Helsinki, FinlandAleksi LehikoinenKainuu Centre for Economic Development, Transport and the Environment, Kajaani, FinlandReima LeinonenUniversity of Helsinki, Helsinki, FinlandHannu PietiäinenDepartment of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, FinlandOtso OvaskainenCentre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, Trondheim, NorwayOtso OvaskainenHelsinki Institute of Life Science, University of Helsinki, Helsinki, FinlandMarjo SaastamoinenDepartment of Mathematics and Statistics, Faculty of Science, University of Helsinki, Helsinki, FinlandJarno VanhataloSpatial Foodweb Ecology Group, Department of Agricultural Sciences, University of Helsinki, Helsinki, FinlandTomas RoslinSpatial Foodweb Ecology Group, Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, SwedenTomas RoslinDepartment of Evolutionary Biology and Environmental Studies, University of Zürich, Zürich, SwitzerlandAnna-Liisa Laine More

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

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