Vertical stratification, climatic seasonality and human disturbances drive the diurnal butterflies (Lepidoptera: Papilionoidea) diversity in the Peruvian Amazon

Vertical stratification, climatic seasonality and human disturbances drive the diurnal butterflies (Lepidoptera: Papilionoidea) diversity in the Peruvian Amazon | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Vertical stratification, climatic seasonality and human disturbances drive the diurnal butterflies (Lepidoptera: Papilionoidea) diversity in the Peruvian Amazon Javier Amaru Castelo, Carolina Milagros Herrera Huayhua, Andrea Valer Canales This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4804716/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Different variables produce changes in the local diversity. They interact complexly to determine the community structure and have a variable effect. In diurnal butterflies, the effect of some variables is confusing due to the contrasting results found, appearing as if there are interactions between them. Most previous works studied, the effect of vertical stratification, climatic seasonality, and human disturbances separately without considering their interaction. In the present work, we evaluated the interaction of these variables using a Box-Cox transformation and Type III ANOVA, and their isolated effect using a Kruskal Wallis test with Dunn Post hoc test. We collected 7655 day-traps from 18 collection points at Manu Learning Centre Biological Station, a forest with a human disturbance gradient, from October 2011 to August 2023 in three different strata (high, medium, and low). We found 378 species from 159 genera. The Type III ANOVA revealed that vertical stratification interacts with the other two variables. In general, the effect of the stratification is negative, being lower in the high stratum. The impact of human disturbance was also negative, being higher in the most preserved forest. Finally, the intermediate climatic season had a greater diversity than the rainy and dry seasons. We concluded that the interaction of the vertical stratification with other variables explained the contrasted result found, the canopy is the last stratum to recover from a disturbance, the species of the high stratum can withstand seasonal variation, and the intermediate season exhibits higher diversity in non-seasonal Amazonian Forest. Interaction ecology Amazonian diversity Diurnal butterflies stratification Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The Amazon is the most widespread continuous forest mass in the world, characterized by its high biological diversity due to its landscape heterogeneity that produces composition changes on a local scale (Dirzo and Raven 2003; Silman 2007). Its western area is regarded as part of the Tropical Andes, considered a hotspot for its high number of endemic vertebrates (1567) and plants (20000) (Myers et al. 2020). A high portion of this diversity is threatened due to habitat loss, direct exploitation, and species introduction, estimating around 1000 species out of a million are extinct in a year (Dirzo and Raven 2003). Knowing the variables that produce changes in the local diversity is necessary to avoid extinctions (Krebs 2014). A large number of variables produce diversity changes on a local scale, such as inter- and intraspecific interactions, dispersion capability, vertical stratification, resource availability, human disturbances, and environmental variables (Paredes et al. 2017; Villa et al. 2019; Cordier et al. 2021; Zhou et al. 2022; Amaru-Castelo et al. 2023; Amaru-Castelo and Marquina-Montesinos 2023). They interact in a complex way to determine the community dynamic and structure, so it is necessary to study them as a complete system (Wootton 1994; Fraker and Peacor 2008). Human disturbance, vertical stratification and climatic seasonality are the most studied variables. The effect of these variables varies according to the studied taxa. Because of the human disturbances, there is a decrease in diversity in amphibians (Cordier et al. 2021), plants (Gómez-Ruiz et al. 2016), diurnal butterflies (Vu et al. 2015; Whitworth et al. 2016), spiders (Mei et al. 2023), centipedes (García-Ruiz 2003; Amaru-Castelo et al. 2024); wasps (Amaru-Castelo and Marquina-Montesinos 2013), mammals (Amaru-Castelo et al. 2023; Mendoza-Soto et al. 2024), and beetles (Spector 2006; Amaru-Castelo et al. 2024). In contrast, an increase is observed in carabids (Castro et al. 2017; Cuellar-Cardozo et al. 2020; Mei et al. 2023; Amaru-Castelo et al. 2024). Because of vertical stratification, a higher diversity at understory is viewed in some Diptera families (Souza-Amorin et al. 2022), Hymenoptera (Souza-Amorin et al. 2022), beetles (Souza-Amorin et al. 2022), spiders (Quijano-Cuervo et al. 2019), and small mammals (Arévalo-Sandi et al. 2021). In contrast, there is a higher diversity at canopy in bugs (Souza-Amorin et al. 2022), sandflies (Leão et al. 2020), and primates (Mendoza-Soto et al. 2024). In diurnal butterflies (Papilionoidea), the effect of the stratification is confusing due to the contrasting results found, appearing that it interacts with other variables, but most studies do not focus on this topic (Whitworth et al. 2016; Souza-Amorin et al. 2022). In each stratum, the climatic variables are different so it is expected that the stratification interacts with the seasonality to determine the diversity, but concluding result are missing due to this could vary from place to place (Nadkarni et al. 2004; Oliveria et al. 2019; Estrada-Villegas et al. 2022). In places with unmarked seasonality, such as the Amazon, changes in the composition for climatic seasonality are observed in wasps (Diniz and Kitayama 1998), amphibians (Ficetola and Maiorano 2016), birds (Somveille et al. 2015), horseflies (Krüger and Krolow 2015), rodents (Rocha et al. 2017); and other do not change as in ants (Montine et al. 2014) and some species of rodents (Rocha et al. 2017). Diurnal butterflies showed that their composition and diversity change in places with flooding seasonality, but in terra firme do not (Oliveira et al. 2023). Lepidoptera are one of the most diverse groups of insects with around 18 000 described species (Kristensen et al. 2007; Liu et al. 2020). Within this, diurnal butterflies have better qualities to study how variables and their interaction affect local diversity, for their fast disturbance responses, the ease of their identification, and their large presence in entomological collections (Gerlach et al. 2013; Chowdhury et al. 2023; Zhou et al. 2022). Most previous works studied the effects of human disturbances, vertical stratification and climate seasonality without taking into consideration their interaction, and from these only Medina et al. 1996 and Whitworth et al. 2016 are located in the Manu Biosphere Reserve (MBR). Although this place has more than 600 species, representing 8.5% of neotropical butterflies (Lamas et al. 1991; Beccaloni and Gaston 1994). In the present work, we studied the effect of three variables (vertical stratification, human disturbance, and climatic seasonality) on the diversity of diurnal butterflies in MBR. The objectives were i) to evaluate the interaction between these variables in modulating the richness and abundance of diurnal butterflies, and ii) to study their isolated effects. We expected that i) these variables interact with each other, especially vertical stratification, due to the contrasting results found in others papers, ii) the understory has a greater richness and abundance than the canopy, iii) disturbance has a negative effect on the richness and abundance, and iv) the rainy season has a greater diversity than the dry season. Materials And Methods Study area The work was conducted at Manu Learning Centre Biological Station (MLC) (12° 47 ’21.52’’ S - 71° 23’ 30.109’’ O), located in the buffer zone at RBM, Manu, Peru. MLC comprises 643 ha of forest in regeneration that could be divided into three areas for their historical disturbances: i) the area with the highest historical disturbance that was completely cleared for high-scale agriculture to 1970 (CCR), ii) the area with median disturbances that was partially cleared for low scale agriculture and selective logging to 1980 (PCR), and iii) the most preserved area with only selective logging to 1990 (Fig. 1). MLC has a warm rainy climate with humidity all year, temperatures between 11°C - 29°C, and annual precipitation between 1200 - 3000 mm (SENAMHI 2021). MLC has two climatic seasons: the humid, characterized by monthly rainfall of more than 200 mm between April and September; and the dry season between May and August (SENAMHI 2021). The seasonal change is gradual, so the months that fall between the boundaries of both seasons have similar characteristics. Here, we recognized three climatic seasons: the dry season from May to August, the rainy season from November to February, and the intermediate season in the rest of the months. Specimen collection and identification We used Van Someren Rydon traps with fermented fish and bananas, placed in three different strata in six different sites of each disturbance gradient (Fig. 1, 2) (similar to Whitworth et al. 2016). The high stratum trap was set between 25 - 30 m from the ground, the medium stratum trap between 10 - 15 m, and the understory trap at 1 m (Fig. 2). The collection was made from October 2011 to August 2023, altering the six collection points of each disturbance gradient. The data was registered six days each week, once every day. The specimens in the trap were identified by comparing the specimens with photographs of previously identified species by Whitworth (2016), comparisons with online databases such as Butterflies of America (https://www.butterfliesofamerica.com), and specialists help for some species with difficult taxonomy. We recorded information about the identification, type of human disturbance gradient, date, vertical stratum, and place. Data analysis We used three categorical variables (stratification, disturbance gradient and climatic season), and two quantitative variables (richness and abundance) of each trap in a collection day. The categorical variables were transformed into indicator variables to be used in a linear model and see if they determine the richness and abundance of diurnal butterflies. Stratification and disturbance gradient were transformed using the orthogonal polynomial contrast with the function contr.poly of stats R package (R Core Team 2013), due to they are ordinal variables; and the climatic season was transformed using a sum contrast with the function contr.sum. These contrasts produce a better result when we use a type III ANOVA to test the differences and significance of variables in a linear model (Fox and Weisberg 2019). Type III ANOVA is used with unbalanced data where an interaction is hypothesized (Fox 2015; Fox and Weisberg 2019). This was made using the car R Package (Fox and Weisberg 2019). Due to the non-normality and non-homoscedasticity of the residuals (Online resource 1), we normalized the model to analyze the interaction and use non-parametric approaches to study differences in the distribution of each qualitative variable. The normality was measured using the Kolmogorov-Smirnov normality test with Lilliefors (KSL) adjustment and plotted in a QQ plot . The homoscedasticity was measured using a Bartlett test and plotted in a dispersion graph of adjusted values and residuals. The KSL is used when we do not assume a known media and variance and is applied with larger data (> 5000) where Shapiro Wilk must not be applied (Zar 2010; Yap 2011). The Bartlett test is biased with non-normal data, so it is necessary to contrast this information with a dispersion plot (Zar 2010). To build the adjusted model, we transformed the richness and abundance in power values using a Box-Cox transformation. This uses a power ( λ ) to approximate the data to normality and reduces the difference of variance (Fox & Weisberg 2019). We selected the λ value that maximizes the logarithmic probability of the parameters ( log.likelihood ), using the boxcox function of MASS R package (Venables 2002). We used Type III ANOVA to measure interactions due to is a robust test that can accept slight deviations of the normality and non-homoscedasticity, especially in large sample size (Zar 2010). The statistically significant interactions were plotted in interaction diagrams using the interaction.plot function of Stats R Package (R Core Team 2013), using the media as reference between groups. The differences in the distribution of the levels in each qualitative variable was measured using a Kruskal Wallis test with a Dunn post hoc test with holm adjustment, due to the fact that they are highly used in non-normal data (Zar 2010). To plot the differences, we used violin plots associated with the richness and abundance. We avoid the atypical data in the plots using the interquartile range to focus on the region that contains most of the data (Vinutha et al. 2018). Finally, the ordinal variables were replaced with discrete numbers (0,1,2), and correlated with the richness and abundance using the Spearman correlation index. Results We used data of 7655 day-traps, finding 378 species of 159 genera (Online resource 2, 3). The most abundant species were Nessaea obrinus with 1277 individuals and Harjesia obscura with 1273 individuals (Online resource 3). We had 43 species with only unique specimens. The adjusted linear model was built using a λ of -0.343 to the richness and -0.303 to the abundance (Fig. 3). In the linear model, we obtained that at least one slope is statistically significant in the richness analysis (F = 27.48, Degrees of freedom = 26 and 7628, P < 0.05), and in the abundance analysis (F = 29.74, Degrees of freedom = 26 and 7628, P < 0.05). The residuals show a median near zero, and symmetric minimum and maximum unlike the unadjusted linear model (Table 1, Online information 1). In the same way, the dispersion and QQ plots show the data is more adjusted to normality and is homoscedastic than the unadjusted model, but they are not normal nor homoscedastic (Fig. 3, Table 1). Table 1. Summary values of the linear model associated with the richness and the abundance, 1°Q. first quartile; 3°Q. third quartile; Med. Median; K. KSL value; K_P. KSL significance; B. Bartlett test value; B_P. Bartlett test significance value Formula Min 1°Q Med. 3°Q Max K K_P B B_P Richness = f (Season x Stratif x Dist) -1.11 -0.5 -0.008 0.5 1.4 0.1 <0.05 101.7 <0.05 Abundance = f (Season x Stratif x Dist) -1.2 0.6 0.005 0.5 1.7 0.1 <0.05 119.4 <0.05 According to the type III ANOVA (Table 2), all the interactions with the vertical stratification were statistically significant (P < 0.05). The interaction of the stratification with the disturbance gradient (Fig. 2a, 2c) produces changes in the diversity difference between disturbance gradients at different strata, but the order is the same, being first SLR, followed by PCR, and finally CCR at all strata. The diversity difference between the disturbance of the highest stratum is wider than the difference in the other strata. Similarly, the interaction between the stratification and the climatic seasonality (Fig. 2b, 2d) changes the diversity difference. The difference between the intermediate season and the other is wider in the low stratum and narrows when we pass to the higher stratum. The richness and abundance difference between the dry and rainy seasons is not significant at the low stratum and increases when going up to the higher stratum, but this is not wider than the differences with the intermediate season. The order is the same in each stratum, with greater diversity in the intermediate season, followed by the dry and rainy seasons respectively. Table 2. Type III ANOVA summary of the linear model associated with the butterflies richness and abundance, showing the significance of each variable and their interactions. Df. Degrees of freedom, F. Fisher statistic, P. significance value Richness Abundance Variables SSq Df F P SSq Df F Pr..F. (Intercept) 3123.9 1 9192.29 <0.05* 4104.3 1 9900.89 <0.05* Seasonality (S) 17.8 2 26.16 <0.05* 24.4 2 29.43 <0.05* Disturbance (D) 68.9 2 101.33 <0.05* 81.2 2 97.98 <0.05* Stratification (St) 151.9 2 223.50 <0.05* 214.6 2 258.80 <0.05* S:D 2.9 4 2.19 0.067 2.8 4 1.70 0.148 S:St 9.9 4 7.26 <0.05* 9.5 4 5.75 <0.05* D:St 9.6 4 7.03 <0.05* 12.2 4 7.33 <0.05* S:D:St 6.9 8 2.57 0.008* 7.8 8 2.36 0.015* Residuals 2592.4 7628 3162.1 7628 * Significant Analyzing only the vertical stratification independently, we found statistically significant differences in the richness (K = 332.36, P < 0.05) and abundance (K = 389.69, P < 0.05), being higher at low stratum (mean richness = 5.01, median richness = 3, mean abundance = 6.75, median abundance = 4 ), followed by the medium stratum (mean richness = 3.46; median richness = 2, mean abundance = 4.24, median abundance = 2), and finally the highest (mean richness = 2.76; median richness = 2, mean abundance = 3.23, median abundance = 2). This decay in the richness and abundance when the stratum increased was also verified in the violin plots (Fig 4). The pairwise analysis revealed a statistical difference (P<0.05) at Dunn post hoc analysis in the richness and abundance (Fig 4). There is a slightly negative correlation between the vertical stratification with the richness (S = -0.22, P < 0.05), and with the abundance (S = -0.12, P < 0.05). In the same way, we observed statistical differences between the levels of human disturbances in the richness analysis ( K = 121.774, P < 0.05) and abundance ( K = 114.189 , P < 0.05), being higher at SLR (mean richness = 4.52, median richness = 3, mean abundance = 5.87, median abundance = 3), followed by PCR (mean richness = 3.88, median richness = 2, mean abundance = 4.87, median abundance = 3), and finally CCR (mean richness = 3.39, median richness = 2, mean abundance = 4.45, median abundance = 2). A decay in the richness and abundance is observed when moving from the least disturbed to the most disturbed at violin plots (Fig. 4). The pairwise analysis with the Dunn post hoc test revealed statistically significant differences between all the levels (P < 0.05). There is a slightly negative correlation between the human disturbances with the richness (S = -0.125, P < 0.05) and with the abundance (S = -0.121, P < 0.05). The dry, intermediate, and rainy seasons also showed statistical differences in the richness (K = 66.237, P < 0.05) and the abundance (K = 67.5105, P < 0.05), being higher at intermediate season (mean richness = 4.807, median richness = 3, mean abundance = 6.42, median abundance = 3), followed by the dry season (mean richness = 3.68, median richness = 2, mean abundance = 4.769, median abundance = 3), and finally the rainy season (mean richness = 3.645, median richness = 2, mean abundance = 4.464, median abundance = 2). A clear higher richness and abundance in the intermediate season was shown in the violin plot (Fig. 4). The pairwise analysis with the Dunn post hoc test revealed statistically significant differences between all the levels (P < 0.05). Discussion and conclusions Here, we studied the impact of human disturbances, vertical stratification, and climatic seasonality on the diurnal butterflies diversity in terms of richness and abundance. Our results indicate that vertical stratification interacts with the other two variables as expected, so this could explain the contrasting results found in other works such as Whitworth et al. 2016 and Souza-Amorin et al. 2022. Focusing on their interaction with the human disturbance gradient, we observed that there was a greater difference in diversity among each gradient at high stratum, which decreased as we moved to the low stratum (Fig. 2), suggesting that the high stratum requires more time to recover from a disturbance event. This response is usual due to the canopy is considered the last level to recover in a succession process (Frelich 2002; Nadkarni et al. 2004). When we observed the interaction with climatic seasonality, it resulted in a pronounced diversity difference between seasons at the low stratum which is reduced as we move to the highest stratum. Given that the composition of diurnal butterflies changes significantly across each stratum, and climatic conditions (temperature and humidity) are more varied in the high stratum, species inhabiting the high stratum should have the capacity to withstand seasonal variations, resulting in similar richness and abundance across them (Lindo and Winchester 2013; Hoenle 2022). Although there was interaction with the stratification, we found that the richness and abundance decrease when the stratum increases in all situations, being greater in the understory. Similar results were found by Barlow et al. (2007), Fordyce and DeVries (2016), and Whitworth et al. (2018), who observed greater diversity and abundance in the understory compared to the canopy. However, Schulze et al. (2001) and Ribeiro and Freitas (2021) documented an opposite pattern. These results could be due to a higher effect in the interaction of this variable due to the more pronounced seasonality and disturbance difference in their study area; the interference between the canopy and understory traps when they are installed at the same point (Ribeiro and Freitas 2021); or the bait used (usually banana and decomposing fish) which is preferred for understory species than canopy species (Schulze et al. 2001). Our results corroborated what was observed by Barlow et al. (2007), Nyafwono et al. (2014), and Montejo-Kovacevich et al. (2018), which indicate that human disturbances have a negative effect on butterfly richness and abundance, making them sensitive species and good indicators of environmental quality. Most butterfly species have larvae that feed on only a few closely related plant species, leading to reduced butterfly diversity when disturbances affect these plants (Kawahara et al. 2023). This negative effect was not clear in the most common species ( Nessaea obrinus and Harjesia obscura) which are equally abundant in all disturbance gradients. N. obrinus is a widespread and common species reported from Colombia to northern Argentina that is usually found in openings, trails, edges, and human inhabited areas of evergreen forests, and are highly abundant all year round without distinction, so they are not sensitive to human disturbances and seasonal changes (Jenkins 1989). H. obscura was also reported in disturbed forests such as cacao plantations, and is a common butterfly of the Amazon (Andrade 2002; Cartagena et al. 2021). For this reason, it was normal that both species were dominant in every condition without distinction. Climate seasonality also proved to be an important factor in determining the diversity. Intermediate seasons (March, April, September, October) exhibit greater richness and abundance, followed by the dry season and finally the rainy season. In non-seasonal Amazonian forests, precipitation and humidity levels remain relatively high throughout the year. During the rainy season (November to March), excessive rainfall floods a significant portion of the soil. This seasonal flood has demonstrated that it leads to changes in species composition and behavior (Ramalho et al. 2021; Oliveira et al. 2023). Additionally, flying insects such as butterflies exhibit reduced movement during periods of constant rain, decreasing the likelihood of being collected. The present study focused on investigating changes in richness and abundance, without taking into account changes in species composition. Future research should analyze how species composition is influenced by these three variables and their interactions. Vertical stratification should be included as an additional variable in all studies of butterfly ecology given its impact on other variables. Furthermore, the effect of these variables on species displacement was also not examined, which is crucial information for understanding the true impact of these three variables on diversity. This work showed the importance of taking in consideration the interaction between variables in ecological studies. Declarations Implications for insect conservation Our study shows the importance of considering the interaction between variables in ecological studies, and points to the value of vertical stratification, disturbance gradient and climatic season as variables that drive the diurnal butterflies diversity. Acknowledgments We thank Q. Meyer and J. C. Cardenas, the founder and CEO, respectively, of Crees Manu, along with their dedicated team, for their invaluable support in facilitating our study. We also extend our appreciation to the T. & J. Meyer Family Foundation for their generous funding contribution to Crees Foundation for Manu. Funding This research was supported by the project “Efecto de las perturbaciones humanas y condiciones climáticas sobre la biodiversidad de la Reserva de Biosfera del Manu” (Resolution N° 012-2023-IC-UAC) funded by “Instituto científico de la Universidad Andina del Cusco”, Cusco. Author contributions All authors contributed to the study conception, design, and writing of the first draft. Data curation and analysis was performed by Javier Amaru-Castelo. All authors commented on previous versions of the manuscript, read, and approved the final manuscript Data availability The raw database, the Python and R code to make the analysis was stored at the GitHub page of the first author (https://github.com/JAmaruCastelo/1-Butterflies-article) and in the Online information. Competing interest The authors declare no competing interests. References Amaru-Castelo J, Marquina-Montesinos E, Herrera-Huayhua C, Yanque-Achata S (2023) Variation of mammal diversity along a gradient separated by geographic barriers within the Andes of Perú. Therya 14(2):1–10. https://doi.org/10.12933/therya-23-4098 Amaru-Castelo J, Marquina-Montesinos E (2023) Diversidad de avispas en un gradiente de perturbación de los ecosistemas de la Reserva de la Biósfera del Manu (Perú): Su valor bioindicador. 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Bartlett test significance value Formula Min 1°Q Med. 3°Q Max K K_P B B_P Richness = f (Season x Stratif x Dist) -1.11 -0.5 -0.008 0.5 1.4 0.1 < 0.05 101.7 < 0.05 Abundance = f (Season x Stratif x Dist) -1.2 0.6 0.005 0.5 1.7 0.1 < 0.05 119.4 < 0.05 Table 2 Type III ANOVA summary of the linear model associated with the butterflies richness and abundance, showing the significance of each variable and their interactions. Df. Degrees of freedom, F. Fisher statistic, P. significance value Richness Abundance Variables SSq Df F P SSq Df F Pr..F. (Intercept) 3123.9 1 9192.29 < 0.05* 4104.3 1 9900.89 < 0.05* Seasonality (S) 17.8 2 26.16 < 0.05* 24.4 2 29.43 < 0.05* Disturbance (D) 68.9 2 101.33 < 0.05* 81.2 2 97.98 < 0.05* Stratification (St) 151.9 2 223.50 < 0.05* 214.6 2 258.80 < 0.05* S:D 2.9 4 2.19 0.067 2.8 4 1.70 0.148 S:St 9.9 4 7.26 < 0.05* 9.5 4 5.75 < 0.05* D:St 9.6 4 7.03 < 0.05* 12.2 4 7.33 < 0.05* S:D:St 6.9 8 2.57 0.008* 7.8 8 2.36 0.015* Residuals 2592.4 7628 3162.1 7628 * Significant Additional Declarations No competing interests reported. Supplementary Files ESM1.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4804716","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":345036941,"identity":"a94e2ad6-c32e-4424-b84b-b408b1612612","order_by":0,"name":"Javier Amaru Castelo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYBACxgYk9gGGCiDFzNyAQzG6FjYGhgMMZ0BakI3BC0BaGNvQbcYCmNtPJ36ubLOTl5/ffODAx3m10fztQC0/KrbhdlhP7mbJs23JhhuOsSUcnLnteO6Mw4wNjD1nbuPxS+4Gyca2A4wb2HgMDvNuO5bbANTCzNiGR0v/280/gVrs57fxfzjMO+dY7nyCWmbkbgPZkthwjIfhMG9DTe4GwlrebrNsOJecvOFYmsHBGccO5G4EajmIzy+G/bmbbzaU2dnObz788MGHmrrceecPH3zwowKPlgZU/mEweQCneiCQR+PX4VM8CkbBKBgFIxQAAGzMZDmCwjXJAAAAAElFTkSuQmCC","orcid":"","institution":"National University of La Plata","correspondingAuthor":true,"prefix":"","firstName":"Javier","middleName":"Amaru","lastName":"Castelo","suffix":""},{"id":345036943,"identity":"b9f0386a-ef26-45ae-aa6b-a345eb9df4e3","order_by":1,"name":"Carolina Milagros Herrera Huayhua","email":"","orcid":"","institution":"Crees Foundation for Manu","correspondingAuthor":false,"prefix":"","firstName":"Carolina","middleName":"Milagros Herrera","lastName":"Huayhua","suffix":""},{"id":345036955,"identity":"9c9403db-8a28-4044-84c3-e1ab678a3b7e","order_by":2,"name":"Andrea Valer Canales","email":"","orcid":"","institution":"Crees Foundation for Manu","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"Valer","lastName":"Canales","suffix":""}],"badges":[],"createdAt":"2024-07-26 01:59:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4804716/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4804716/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63372338,"identity":"cf72aac1-9988-4704-a406-14f86363f476","added_by":"auto","created_at":"2024-08-27 12:07:07","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":748302,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area showing the three types of areas with different historic human disturbances and collection points. CCR. Completely cleared now in regeneration; PCR. Partially cleared now in regeneration; and SLR. Selective logged now in regeneration\u003c/p\u003e","description":"","filename":"image1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4804716/v1/63662f99181671fbcc91b700.jpg"},{"id":63372920,"identity":"239f5c04-f37a-4f01-a3a0-9bb99d2096a4","added_by":"auto","created_at":"2024-08-27 12:15:07","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":74232,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction plot of climatic seasonality and human disturbance gradient with the vertical stratification. L. low stratum, M. medium stratum, H. high stratum, SLR. Selective logged now in regeneration, PCR. Partially cleared now in regeneration, CCR. Completely cleared now in regeneration. a) interaction between vertical stratification and disturbance gradient using the richness, b) interaction between vertical stratification and vertical stratification using the richness, c) interaction between vertical stratification and disturbance gradient using the abundance, d) interaction between vertical stratification and vertical stratification using the abundance\u003c/p\u003e","description":"","filename":"image2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4804716/v1/6d79a3d8367f9953e8a9b836.jpg"},{"id":63372337,"identity":"75ff0e70-5af7-48c7-ada7-5eff649e6a7f","added_by":"auto","created_at":"2024-08-27 12:07:07","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":58714,"visible":true,"origin":"","legend":"\u003cp\u003eLogarithmic probabilities (log.likelihood) to different values of \u003cem\u003eλ \u003c/em\u003epower used in the Box-Cox transformation, a) for the richness analysis, d) for the abundance analysis; QQ plot of the linear model, b) for the richness, e) for the abundance; dispersion diagram, c) for the richness analysis, f) for the abundance\u003c/p\u003e","description":"","filename":"image3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4804716/v1/9c58b148d6acdbb0f38c961a.jpg"},{"id":63372339,"identity":"3f7e2435-293e-4c4d-8917-8ba46377a83a","added_by":"auto","created_at":"2024-08-27 12:07:07","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":92784,"visible":true,"origin":"","legend":"\u003cp\u003eViolín plots comparing the distribution in each level of vertical stratification (a, b); human disturbances (c, d), and climatic seasonality (e, f) using the richness (a, c, e) and abundance (b, d, f) of diurnal butterflies in each day trap\u003c/p\u003e","description":"","filename":"image4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4804716/v1/cc5207fa5235a4e95ab62b63.jpg"},{"id":63867220,"identity":"c1062f3e-cf64-4191-ba08-2a48413f0b42","added_by":"auto","created_at":"2024-09-03 07:39:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1549661,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4804716/v1/e6710b62-10a0-4c13-9581-df6d07c27004.pdf"},{"id":63372341,"identity":"9b1230ca-6f4e-4ac2-80f3-3f827a0ef8c9","added_by":"auto","created_at":"2024-08-27 12:07:07","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":203805,"visible":true,"origin":"","legend":"","description":"","filename":"ESM1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4804716/v1/938c66c89d2490dcc915198d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Vertical stratification, climatic seasonality and human disturbances drive the diurnal butterflies (Lepidoptera: Papilionoidea) diversity in the Peruvian Amazon","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Amazon is the most widespread continuous forest mass in the world, characterized by its high biological diversity due to its landscape heterogeneity that produces composition changes on a local scale (Dirzo and Raven 2003; Silman 2007). Its western area is regarded as part of the Tropical Andes, considered a hotspot for its high number of endemic vertebrates (1567) and plants (20000) (Myers et al. 2020). A high portion of this diversity is threatened due to habitat loss, direct exploitation, and species introduction, estimating around 1000 species out of a million are extinct in a year (Dirzo and Raven 2003). Knowing the variables that produce changes in the local diversity is necessary to avoid extinctions (Krebs 2014).\u003c/p\u003e\n\u003cp\u003eA large number of variables produce diversity changes on a local scale, such as inter- and intraspecific interactions, dispersion capability, vertical stratification, resource availability, human disturbances, and environmental variables (Paredes et al. 2017; Villa et al. 2019; Cordier et al. 2021; Zhou et al. 2022; Amaru-Castelo et al. 2023; Amaru-Castelo and Marquina-Montesinos 2023). They interact in a complex way to determine the community dynamic and structure, so it is necessary to study them as a complete system (Wootton 1994; Fraker and Peacor 2008). Human disturbance, vertical stratification and climatic seasonality are the most studied variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe effect of these variables varies according to the studied taxa. Because of the human disturbances, there is a decrease in diversity in amphibians (Cordier et al. 2021), plants (G\u0026oacute;mez-Ruiz et al. 2016), diurnal butterflies (Vu et al. 2015; Whitworth et al. 2016), spiders (Mei et al. 2023), centipedes (Garc\u0026iacute;a-Ruiz 2003; Amaru-Castelo et al. 2024); wasps (Amaru-Castelo and Marquina-Montesinos 2013), mammals (Amaru-Castelo et al. 2023; Mendoza-Soto et al. 2024), and beetles (Spector 2006; Amaru-Castelo et al. 2024). In contrast, an increase is observed in carabids (Castro et al. 2017; Cuellar-Cardozo et al. 2020; Mei et al. 2023; Amaru-Castelo et al. 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBecause of vertical stratification, a higher diversity at understory is viewed in some Diptera families (Souza-Amorin et al. 2022), Hymenoptera (Souza-Amorin et al. 2022), beetles (Souza-Amorin et al. 2022), spiders (Quijano-Cuervo et al. 2019), and small mammals (Ar\u0026eacute;valo-Sandi et al. 2021). In contrast, there is a higher diversity at canopy in bugs (Souza-Amorin et al. 2022), sandflies (Le\u0026atilde;o et al. 2020), and primates (Mendoza-Soto et al. 2024). In diurnal butterflies (Papilionoidea), the effect of the stratification is confusing due to the contrasting results found, appearing that it interacts with other variables, but most studies do not focus on this topic (Whitworth et al. 2016; Souza-Amorin et al. 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn each stratum, the climatic variables are different so it is expected that the stratification interacts with the seasonality to determine the diversity, but concluding result are missing due to this could vary from place to place (Nadkarni et al. 2004; Oliveria et al. 2019; Estrada-Villegas et al. 2022). In places with unmarked seasonality, such as the Amazon, changes in the composition for climatic seasonality are observed in wasps (Diniz and Kitayama 1998), amphibians (Ficetola and Maiorano 2016), birds (Somveille et al. 2015), horseflies (Kr\u0026uuml;ger and Krolow 2015), rodents (Rocha et al. 2017); and other do not change as in ants (Montine et al. 2014) and some species of rodents (Rocha et al. 2017). Diurnal butterflies showed that their composition and diversity change in places with flooding seasonality, but in terra firme do not (Oliveira et al. 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLepidoptera are one of the most diverse groups of insects with around 18 000 described species (Kristensen et al. 2007; Liu et al. 2020). Within this, diurnal butterflies have better qualities to study how variables and their interaction affect local diversity, for their fast disturbance responses, the ease of their identification, and their large presence in entomological collections (Gerlach et al. 2013; Chowdhury et al. 2023; Zhou et al. 2022). Most previous works studied the effects of human disturbances, vertical stratification and climate seasonality without taking into consideration their interaction, and from these only Medina et al. 1996 and Whitworth et al. 2016 are located in the Manu Biosphere Reserve (MBR). Although this place has more than 600 species, representing 8.5% of neotropical butterflies (Lamas et al. 1991; Beccaloni and Gaston 1994).\u003c/p\u003e\n\u003cp\u003eIn the present work, we studied the effect of three variables (vertical stratification, human disturbance, and climatic seasonality) on the diversity of diurnal butterflies in MBR. The objectives were i) to evaluate the interaction between these variables in modulating the richness and abundance of diurnal butterflies, and ii) to study their isolated effects. We expected that i) these variables interact with each other, especially vertical stratification, due to the contrasting results found in others papers, ii) the understory has a greater richness and abundance than the canopy, iii) disturbance has a negative effect on the richness and abundance, and iv) the rainy season has a greater diversity than the dry season.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work was conducted at Manu Learning Centre Biological Station (MLC) (12\u0026deg; 47 \u0026rsquo;21.52\u0026rsquo;\u0026rsquo; S - 71\u0026deg; 23\u0026rsquo; 30.109\u0026rsquo;\u0026rsquo; O), located in the buffer zone at RBM, Manu, Peru. MLC comprises 643 ha of forest in regeneration that could be divided into three areas for their historical disturbances: i) the area with the highest historical disturbance that was completely cleared for high-scale agriculture to 1970 (CCR), ii) the area with median disturbances that was partially cleared for low scale agriculture and selective logging to 1980 (PCR), and iii) the most preserved area with only selective logging to 1990 (Fig. 1).\u003c/p\u003e\n\u003cp\u003eMLC has a warm rainy climate with humidity all year, temperatures between 11\u0026deg;C - 29\u0026deg;C, and annual precipitation between 1200 - 3000 mm (SENAMHI 2021). MLC has two climatic seasons: the humid, characterized by monthly rainfall of more than 200 mm between April and September; and the dry season between May and August (SENAMHI 2021). The seasonal change is gradual, so the months that fall between the boundaries of both seasons have similar characteristics. \u0026nbsp; Here, we recognized three climatic seasons: the dry season from May to August, the rainy season from November to February, and the intermediate season in the rest of the months.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpecimen collection and identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used Van Someren Rydon traps with fermented fish and bananas, placed in three different strata in six different sites of each disturbance gradient (Fig. 1, 2) (similar to Whitworth et al. 2016). The high stratum trap was set between 25 - 30 m from the ground, the medium stratum trap between 10 - 15 m, and the understory trap at 1 m (Fig. 2). The collection was made from October 2011 to August 2023, altering the six collection points of each disturbance gradient. The data was registered six days each week, once every day. The specimens in the trap were identified by comparing the specimens with photographs of previously identified species by Whitworth (2016), comparisons with online databases such as Butterflies of America (https://www.butterfliesofamerica.com), and specialists help for some species with difficult taxonomy. We recorded information about the identification, type of human disturbance gradient, date, vertical stratum, and place.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used three categorical variables (stratification, disturbance gradient and climatic season), and two quantitative variables (richness and abundance) of each trap in a collection day. The categorical variables were transformed into indicator variables to be used in a linear model and see if they determine the richness and abundance of diurnal butterflies. Stratification and disturbance gradient were transformed using the orthogonal polynomial contrast with the function \u003cem\u003econtr.poly\u003c/em\u003e of stats R package (R Core Team 2013), due to they are ordinal variables; and the climatic season was transformed using a sum contrast with the function \u003cem\u003econtr.sum.\u0026nbsp;\u003c/em\u003eThese contrasts produce a better result when we use a type III ANOVA to test the differences and significance of variables in a linear model (Fox and Weisberg 2019). Type III ANOVA is used with unbalanced data where an interaction is hypothesized (Fox 2015; Fox and Weisberg 2019). This was made using the car R Package (Fox and Weisberg 2019).\u003c/p\u003e\n\u003cp\u003eDue to the non-normality and non-homoscedasticity of the residuals (Online resource 1), we normalized the model to analyze the interaction and use non-parametric approaches to study differences in the distribution of each qualitative variable. The normality was measured using the Kolmogorov-Smirnov normality test with Lilliefors (KSL) adjustment and plotted in a \u003cem\u003eQQ plot\u003c/em\u003e. The homoscedasticity was measured using a Bartlett test and plotted in a dispersion graph of adjusted values and residuals. The KSL is used when we do not assume a known media and variance and is applied with larger data (\u0026gt; 5000) where Shapiro Wilk must not be applied (Zar 2010; Yap 2011). The Bartlett test is biased with non-normal data, so it is necessary to contrast this information with a dispersion plot (Zar 2010).\u003c/p\u003e\n\u003cp\u003eTo build\u0026nbsp;the adjusted model, we transformed the richness and abundance in power values using a Box-Cox transformation. This uses a power (\u003cem\u003e\u0026lambda;\u003c/em\u003e) to approximate the data to normality and reduces the difference of variance (Fox \u0026amp; Weisberg 2019). We selected the \u003cem\u003e\u0026lambda;\u0026nbsp;\u003c/em\u003evalue that maximizes the logarithmic probability of the parameters (\u003cem\u003elog.likelihood\u003c/em\u003e), using the \u003cem\u003eboxcox\u003c/em\u003e function of MASS R package (Venables 2002). We used Type III ANOVA to measure interactions due to is a robust test that can accept slight deviations of the normality and non-homoscedasticity, especially in large sample size (Zar 2010). The statistically significant interactions were plotted in interaction diagrams using the \u003cem\u003einteraction.plot\u003c/em\u003e function of Stats R Package (R Core Team 2013), using the media as reference between groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe differences in the distribution of the levels in each qualitative variable was measured using a Kruskal Wallis test with a Dunn post hoc test with \u003cem\u003eholm\u003c/em\u003e adjustment, due to the fact that they are highly used in non-normal data \u0026nbsp;(Zar 2010). To plot the differences, we used violin plots associated with the richness and abundance. We avoid the atypical data in the plots using the interquartile range to focus on the region that contains most of the data (Vinutha et al. 2018). Finally, the ordinal variables were replaced with discrete numbers (0,1,2), and correlated with the richness and abundance using the Spearman correlation index.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe used data of 7655 day-traps, finding 378 species of 159 genera (Online resource 2, 3). The most abundant species were \u003cem\u003eNessaea obrinus\u003c/em\u003e with 1277 individuals and \u003cem\u003eHarjesia obscura\u003c/em\u003e with 1273 individuals (Online resource 3). We had 43 species with only unique specimens. The adjusted linear model was built using a \u0026nbsp;\u003cem\u003e\u0026lambda;\u0026nbsp;\u003c/em\u003eof -0.343 to the richness and -0.303 to the abundance (Fig. 3). In the linear model, we obtained that at least one slope is statistically significant in the richness analysis (F = 27.48, Degrees of freedom = 26 and 7628, P \u0026lt; 0.05), and in the abundance analysis (F = 29.74, Degrees of freedom \u0026nbsp; = 26 and 7628, P \u0026lt; 0.05). The residuals show a median near zero, and symmetric minimum and maximum unlike the unadjusted linear model (Table 1, Online information 1). In the same way, the dispersion and QQ plots show the data is more adjusted to normality and is homoscedastic than the unadjusted model, but they are not normal nor homoscedastic (Fig. 3, Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Summary values of the linear model associated with the richness and the abundance, 1\u0026deg;Q. first quartile; 3\u0026deg;Q. third quartile; Med. Median; K. KSL value; K_P. KSL significance; B. Bartlett test value; B_P. Bartlett test significance value\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.57331136738056%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFormula\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.260296540362439%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.095551894563426%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u0026deg;Q\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.907742998352553%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMed.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436573311367381%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u0026deg;Q\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.260296540362439%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.930807248764415%\"\u003e\n \u003cp\u003e\u003cstrong\u003eK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248764415156508%\"\u003e\n \u003cp\u003e\u003cstrong\u003eK_P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.919275123558484%\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367380560131796%\"\u003e\n \u003cp\u003e\u003cstrong\u003eB_P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.57331136738056%\"\u003e\n \u003cp\u003e\u003cem\u003eRichness = f (Season x Stratif x Dist)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.260296540362439%\"\u003e\n \u003cp\u003e-1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.095551894563426%\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.907742998352553%\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436573311367381%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.260296540362439%\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.930807248764415%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248764415156508%\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.919275123558484%\"\u003e\n \u003cp\u003e101.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367380560131796%\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.57331136738056%\"\u003e\n \u003cp\u003e\u003cem\u003eAbundance = f (Season x Stratif x Dist)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.260296540362439%\"\u003e\n \u003cp\u003e-1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.095551894563426%\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.907742998352553%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.436573311367381%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.260296540362439%\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.930807248764415%\"\u003e\n \u003cp\u003e0.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248764415156508%\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.919275123558484%\"\u003e\n \u003cp\u003e119.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367380560131796%\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAccording to the type III ANOVA (Table 2), all the interactions with the vertical stratification were statistically significant (P \u0026lt; 0.05). The interaction of the stratification with the disturbance gradient (Fig. 2a, 2c) produces changes in the diversity difference between disturbance gradients at different strata, but the order is the same, being first SLR, followed by PCR, and finally CCR at all strata. The diversity difference between the disturbance of the highest stratum is wider than the difference in the other strata. Similarly, the interaction between the stratification and the climatic seasonality (Fig. 2b, 2d) changes the diversity difference. The difference between the intermediate season and the other is wider in the low stratum and narrows when we pass to the higher stratum. The richness and abundance difference between the dry and rainy seasons is not significant at the low stratum and increases when going up to the higher stratum, but this is not wider than the differences with the intermediate season. The order is the same in each stratum, with greater diversity in the intermediate season, followed by the dry and rainy seasons respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Type III ANOVA summary of the linear model associated with the butterflies richness and abundance, showing the significance of each variable and their interactions. Df. Degrees of freedom, F. Fisher statistic, P. significance value\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"539\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.00556586270872%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.40445269016698%\" colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eRichness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.5899814471243%\" colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbundance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.00556586270872%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.276437847866418%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSq\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.719851576994435%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.575139146567718%\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.833024118738404%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.575139146567718%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSq\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2356215213358075%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.760667903525047%\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.018552875695732%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePr..F.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.00556586270872%\"\u003e\n \u003cp\u003e\u003cstrong\u003e(Intercept)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.276437847866418%\"\u003e\n \u003cp\u003e3123.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.719851576994435%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.575139146567718%\"\u003e\n \u003cp\u003e9192.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.833024118738404%\"\u003e\n \u003cp\u003e\u0026lt;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.575139146567718%\"\u003e\n \u003cp\u003e4104.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2356215213358075%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.760667903525047%\"\u003e\n \u003cp\u003e9900.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.018552875695732%\"\u003e\n \u003cp\u003e\u0026lt;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.00556586270872%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeasonality (S)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.276437847866418%\"\u003e\n \u003cp\u003e17.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.719851576994435%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.575139146567718%\"\u003e\n \u003cp\u003e26.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.833024118738404%\"\u003e\n \u003cp\u003e\u0026lt;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.575139146567718%\"\u003e\n \u003cp\u003e24.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2356215213358075%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.760667903525047%\"\u003e\n \u003cp\u003e29.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.018552875695732%\"\u003e\n \u003cp\u003e\u0026lt;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.00556586270872%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisturbance (D)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.276437847866418%\"\u003e\n \u003cp\u003e68.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.719851576994435%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.575139146567718%\"\u003e\n \u003cp\u003e101.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.833024118738404%\"\u003e\n \u003cp\u003e\u0026lt;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.575139146567718%\"\u003e\n \u003cp\u003e81.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2356215213358075%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.760667903525047%\"\u003e\n \u003cp\u003e97.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.018552875695732%\"\u003e\n \u003cp\u003e\u0026lt;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.00556586270872%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStratification (St)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.276437847866418%\"\u003e\n \u003cp\u003e151.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.719851576994435%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.575139146567718%\"\u003e\n \u003cp\u003e223.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.833024118738404%\"\u003e\n \u003cp\u003e\u0026lt;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.575139146567718%\"\u003e\n \u003cp\u003e214.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2356215213358075%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.760667903525047%\"\u003e\n \u003cp\u003e258.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.018552875695732%\"\u003e\n \u003cp\u003e\u0026lt;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.00556586270872%\"\u003e\n \u003cp\u003e\u003cstrong\u003eS:D\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.276437847866418%\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.719851576994435%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.575139146567718%\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.833024118738404%\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.575139146567718%\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2356215213358075%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.760667903525047%\"\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.018552875695732%\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.00556586270872%\"\u003e\n 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\u003cp\u003e\u003cstrong\u003eS:D:St\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.276437847866418%\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.719851576994435%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.575139146567718%\"\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.833024118738404%\"\u003e\n \u003cp\u003e0.008*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.575139146567718%\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2356215213358075%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.760667903525047%\"\u003e\n \u003cp\u003e2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.018552875695732%\"\u003e\n \u003cp\u003e0.015*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.00556586270872%\"\u003e\n \u003cp\u003e\u003cstrong\u003eResiduals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.276437847866418%\"\u003e\n \u003cp\u003e2592.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.719851576994435%\"\u003e\n \u003cp\u003e7628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.575139146567718%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.833024118738404%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.575139146567718%\"\u003e\n \u003cp\u003e3162.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2356215213358075%\"\u003e\n \u003cp\u003e7628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.760667903525047%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.018552875695732%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e* Significant\u003c/p\u003e\n\u003cp\u003eAnalyzing only the vertical stratification independently, we found statistically significant differences in the richness (K = 332.36, P \u0026lt; 0.05) and abundance (K = 389.69, P \u0026lt; 0.05), being higher at low stratum (mean richness = 5.01, median richness = 3, mean abundance = 6.75, median abundance = 4 ), followed by the medium stratum (mean richness = 3.46; median richness = 2, mean abundance = 4.24, median abundance = 2), and finally the highest (mean richness = 2.76; median richness = 2, mean abundance = 3.23, median abundance = 2). This decay in the richness and abundance when the stratum increased was also verified in the violin plots (Fig 4). The pairwise analysis revealed a statistical difference (P\u0026lt;0.05) at Dunn \u003cem\u003epost hoc\u0026nbsp;\u003c/em\u003eanalysis in the richness and abundance (Fig 4). There is a slightly negative correlation between the vertical stratification with the richness (S = -0.22, P \u0026lt; 0.05), and with the abundance (S = -0.12, P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eIn the same way, we observed statistical differences between the levels of human disturbances in the richness analysis ( K = 121.774, P \u0026lt; 0.05) and abundance ( K = 114.189 , P \u0026lt; 0.05), being higher at SLR (mean richness = 4.52, median richness = 3, mean abundance = 5.87, median abundance = 3), followed by PCR (mean richness = 3.88, median richness = 2, mean abundance = 4.87, median abundance = 3), and finally CCR (mean richness = 3.39, median richness = 2, mean abundance = 4.45, median abundance = 2). A decay in the richness and abundance is observed when moving from the least disturbed to the most disturbed at violin plots (Fig. \u0026nbsp;4). The pairwise analysis with the Dunn \u003cem\u003epost hoc\u0026nbsp;\u003c/em\u003etest revealed statistically significant differences between all the levels (P \u0026lt; 0.05). There is a slightly negative correlation between the human disturbances with the richness (S = -0.125, P \u0026lt; 0.05) and with the abundance (S = -0.121, P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eThe dry, intermediate, and rainy seasons also showed statistical differences in the richness (K = 66.237, P \u0026lt; 0.05) and the abundance \u0026nbsp; (K = 67.5105, P \u0026lt; 0.05), being higher at intermediate season (mean richness = 4.807, median richness = 3, mean abundance = 6.42, median abundance = 3), followed by the dry season (mean richness = 3.68, median richness \u0026nbsp;= 2, mean abundance = 4.769, median abundance = 3), and finally the rainy season (mean richness = 3.645, median richness = 2, mean abundance = 4.464, median abundance = 2). A clear higher richness and abundance in the intermediate season was shown in the violin plot (Fig. 4). The pairwise analysis with the Dunn \u003cem\u003epost hoc\u0026nbsp;\u003c/em\u003etest revealed statistically significant differences between all the levels (P \u0026lt; 0.05).\u003c/p\u003e"},{"header":"Discussion and conclusions","content":"\u003cp\u003eHere, we studied the impact of human disturbances, vertical stratification, and climatic seasonality on the diurnal butterflies diversity in terms of richness and abundance. Our results indicate that vertical stratification interacts with the other two variables as expected, so this could explain the contrasting results found in other works such as Whitworth et al. 2016 and Souza-Amorin et al. 2022. Focusing on their interaction with the human disturbance gradient, we observed that there was a greater difference in diversity among each gradient at high stratum, which decreased as we moved to the low stratum (Fig. 2), suggesting that the high stratum requires more time to recover from a disturbance event. This response is usual due to the canopy is considered the last level to recover in a succession process (Frelich 2002; Nadkarni et al. 2004).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen we observed the interaction with climatic seasonality, it resulted in a pronounced diversity difference between seasons at the low stratum which is reduced as we move to the highest stratum. Given that the composition of diurnal butterflies changes significantly across each stratum, and climatic conditions (temperature and humidity) are more varied in the high stratum, species inhabiting the high stratum should have the capacity to withstand seasonal variations, resulting in similar richness and abundance across them (Lindo and Winchester 2013; Hoenle 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough there was interaction with the stratification, we found that the richness and abundance decrease when the stratum increases in all situations, being greater in the understory. Similar results were found by Barlow et al. (2007), Fordyce and DeVries (2016), and Whitworth et al. (2018), who observed greater diversity and abundance in the understory compared to the canopy. However, Schulze et al. (2001) and Ribeiro and Freitas (2021) documented an opposite pattern. These results could be due to a higher effect in the interaction of this variable due to the more pronounced seasonality and disturbance difference in their study area; the interference between the canopy and understory traps when they are installed at the same point (Ribeiro and Freitas 2021); or the bait used (usually banana and decomposing fish) which is preferred for understory species than canopy species (Schulze et al. 2001).\u003c/p\u003e\n\u003cp\u003eOur results corroborated what was observed by Barlow et al. (2007), Nyafwono et al. (2014), and Montejo-Kovacevich et al. (2018), which indicate that human disturbances have a negative effect on butterfly richness and abundance, making them sensitive species and good indicators of environmental quality. Most butterfly species have larvae that feed on only a few closely related plant species, leading to reduced butterfly diversity when disturbances affect these plants (Kawahara et al. 2023).\u003c/p\u003e\n\u003cp\u003eThis negative effect was not clear in the most common species (\u003cem\u003eNessaea obrinus\u0026nbsp;\u003c/em\u003eand \u003cem\u003eHarjesia obscura)\u0026nbsp;\u003c/em\u003ewhich\u003cem\u003e\u0026nbsp;\u003c/em\u003eare equally abundant in all disturbance gradients. \u003cem\u003eN. obrinus\u003c/em\u003e is a widespread and common species reported from Colombia to northern Argentina that is usually found in openings, trails, edges, and human inhabited areas of evergreen forests, and are highly abundant all year round without distinction, so they are not sensitive to human disturbances and seasonal changes (Jenkins 1989). \u003cem\u003eH. obscura\u003c/em\u003e was also reported in disturbed forests such as cacao plantations, and is a common butterfly of the Amazon (Andrade 2002; Cartagena et al. 2021). For this reason, it was normal that both species were dominant in every condition without distinction. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClimate seasonality also proved to be an important factor in determining the diversity. Intermediate seasons (March, April, September, October) exhibit greater richness and abundance, followed by the dry season and finally the rainy season. In non-seasonal Amazonian forests, precipitation and humidity levels remain relatively high throughout the year. During the rainy season (November to March), excessive rainfall floods a significant portion of the soil. This seasonal flood has demonstrated that it leads to changes in species composition and behavior (Ramalho et al. 2021; Oliveira et al. 2023). Additionally, flying insects such as butterflies exhibit reduced movement during periods of constant rain, decreasing the likelihood of being collected.\u003c/p\u003e\n\u003cp\u003eThe present study focused on investigating changes in richness and abundance, without taking into account changes in species composition. Future research should analyze how species composition is influenced by these three variables and their interactions. Vertical stratification should be included as an additional variable in all studies of butterfly ecology given its impact on other variables. Furthermore, the effect of these variables on species displacement was also not examined, which is crucial information for understanding the true impact of these three variables on diversity. This work showed the importance of taking in consideration the interaction between variables in ecological studies.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eImplications for insect conservation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study shows the importance of considering the interaction between variables in ecological studies, and points to the value of vertical stratification, disturbance gradient and climatic season as variables that drive the diurnal butterflies diversity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Q. Meyer and J. C. Cardenas, the founder and CEO, respectively, of Crees Manu, along with their dedicated team, for their invaluable support in facilitating our study. We also extend our appreciation to the T. \u0026amp; \u0026nbsp;J. Meyer Family Foundation for their generous funding contribution to Crees Foundation for Manu.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the project \u0026ldquo;Efecto de las perturbaciones humanas y condiciones clim\u0026aacute;ticas sobre la biodiversidad de la Reserva de Biosfera del Manu\u0026rdquo; (Resolution N\u0026deg; 012-2023-IC-UAC) funded by \u0026ldquo;Instituto cient\u0026iacute;fico de la Universidad Andina del Cusco\u0026rdquo;, Cusco.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception, design, and writing of the first draft. Data curation and analysis was performed by Javier Amaru-Castelo. All authors commented on previous versions of the manuscript, read, and approved the final manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw database, the Python and R code to make the analysis was stored at the GitHub page of the first author (https://github.com/JAmaruCastelo/1-Butterflies-article) and in the Online information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmaru-Castelo J, Marquina-Montesinos E, Herrera-Huayhua C, Yanque-Achata S (2023) Variation of mammal diversity along a gradient separated by geographic barriers within the Andes of Per\u0026uacute;. Therya 14(2):1\u0026ndash;10. https://doi.org/10.12933/therya-23-4098\u003c/li\u003e\n\u003cli\u003eAmaru-Castelo J, Marquina-Montesinos E (2023) Diversidad de avispas en un gradiente de perturbaci\u0026oacute;n de los ecosistemas de la Reserva de la Bi\u0026oacute;sfera del Manu (Per\u0026uacute;): Su valor bioindicador. Ecol Austral \u003cem\u003e33\u003c/em\u003e:598\u0026ndash;608. https://doi.org/http://dx.doi.org/10.25260/EA.23.33.2.0.2159\u003c/li\u003e\n\u003cli\u003eAmaru-Castelo J, Echevarria-Macassi LA, Marquina-Montesinos E, Herrera-Huayhua CM, Bautista-Challco B (2024) Invertebrates as disturbance bioindicators in the Manu Biosphere Reserve. Rev Biol Trop 72(1): e56199. https://doi.org/10.15517/rev.biol.trop.v72i1.56199\u003c/li\u003e\n\u003cli\u003eAndrade MG (2002) Biodiversidad de Las Mariposas (Lepidoptera: Rhopalocera) de Colombia. 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Rainforest carrion-feeding butterflies are more sensitive indicators of disturbance history than fruit feeders. Biol Conserv 217:383-390. https://doi.org/10.1016/j.biocon.2017.11.030 \u003c/li\u003e\n\u003cli\u003eWhitworth A, Villacampa J, Brown A, Huarcaya RP, Downie R, MacLeod R (2016) Past human disturbance effects upon biodiversity are greatest in the canopy. A case study on rainforest butterflies. PLoS ONE 11(3):1-20 https://doi.org/10.1371/journal.pone.0150520\u003c/li\u003e\n\u003cli\u003eWootton JT (1994) Putting the pieces together: Testing the independence of interactions among organisms. Ecology 75(6):1544\u0026ndash;1551. https://doi.org/10.2307/1939615\u003c/li\u003e\n\u003cli\u003eYap BW, Sim CH (2011) Comparisons of various types of normality tests. \u003cem\u003eJ Stat Comput Sim\u003c/em\u003e \u003cem\u003e81\u003c/em\u003e(12): 2141\u0026ndash;2155. https://doi.org/10.1080/00949655.2010.520163\u003c/li\u003e\n\u003cli\u003eZar J (2010) Biostatistical Analysis. Pearson, London\u003c/li\u003e\n\u003cli\u003eZhou S, Wang K, Messyasz B, Xu Y, Gao M, Li Y, Wu N (2022) Functional and taxonomic beta diversity of butterfly assemblages in an archipelago: relative importance of island characteristics, climate, and spatial factors. Ecol Indic 142:109191. https://doi.org/10.1016/j.ecolind.2022.109191\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSummary values of the linear model associated with the richness and the abundance, 1\u0026deg;Q. first quartile; 3\u0026deg;Q. third quartile; Med. Median; K. KSL value; K_P. KSL significance; B. Bartlett test value; B_P. Bartlett test significance value\u003c/div\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eFormula\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eMin\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1\u0026deg;Q\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eMed.\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e3\u0026deg;Q\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eMax\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eK\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eK_P\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eB\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eB_P\u003c/div\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eRichness\u0026thinsp;=\u0026thinsp;f (Season x Stratif x Dist)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e-1.11\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e-0.5\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e-0.008\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.5\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1.4\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.1\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.05\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e101.7\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.05\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eAbundance\u0026thinsp;=\u0026thinsp;f (Season x Stratif x Dist)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e-1.2\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.6\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.005\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.5\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1.7\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.1\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.05\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e119.4\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.05\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eType III ANOVA summary of the linear model associated with the butterflies richness and abundance, showing the significance of each variable and their interactions. Df. Degrees of freedom, F. Fisher statistic, P. significance value\u003c/div\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eRichness\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eAbundance\u003c/div\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eVariables\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSSq\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eDf\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eF\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eP\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSSq\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eDf\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eF\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003ePr..F.\u003c/div\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e(Intercept)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e3123.9\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e9192.29\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.05*\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4104.3\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e9900.89\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.05*\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eSeasonality (S)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e17.8\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e26.16\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.05*\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e24.4\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e29.43\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.05*\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eDisturbance (D)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e68.9\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e101.33\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.05*\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e81.2\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e97.98\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.05*\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eStratification (St)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e151.9\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e223.50\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.05*\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e214.6\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e258.80\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.05*\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eS:D\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2.9\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2.19\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.067\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2.8\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1.70\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.148\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eS:St\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e9.9\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e7.26\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv 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\u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Interaction, ecology, Amazonian, diversity, Diurnal butterflies, stratification","lastPublishedDoi":"10.21203/rs.3.rs-4804716/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4804716/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDifferent variables produce changes in the local diversity. They interact complexly to determine the community structure and have a variable effect. In diurnal butterflies, the effect of some variables is confusing due to the contrasting results found, appearing as if there are interactions between them. Most previous works studied, the effect of vertical stratification, climatic seasonality, and human disturbances separately without considering their interaction. In the present work, we evaluated the interaction of these variables using a Box-Cox transformation and Type III ANOVA, and their isolated effect using a Kruskal Wallis test with Dunn Post hoc test. We collected 7655 day-traps from 18 collection points at Manu Learning Centre Biological Station, a forest with a human disturbance gradient, from October 2011 to August 2023 in three different strata (high, medium, and low). We found 378 species from 159 genera. The Type III ANOVA revealed that vertical stratification interacts with the other two variables. In general, the effect of the stratification is negative, being lower in the high stratum. The impact of human disturbance was also negative, being higher in the most preserved forest. Finally, the intermediate climatic season had a greater diversity than the rainy and dry seasons. We concluded that the interaction of the vertical stratification with other variables explained the contrasted result found, the canopy is the last stratum to recover from a disturbance, the species of the high stratum can withstand seasonal variation, and the intermediate season exhibits higher diversity in non-seasonal Amazonian Forest.\u003c/p\u003e","manuscriptTitle":"Vertical stratification, climatic seasonality and human disturbances drive the diurnal butterflies (Lepidoptera: Papilionoidea) diversity in the Peruvian Amazon","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-27 12:07:02","doi":"10.21203/rs.3.rs-4804716/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1bd6dcde-2a64-4f7f-851c-dd0e5907ff16","owner":[],"postedDate":"August 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-03T07:31:23+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-27 12:07:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4804716","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4804716","identity":"rs-4804716","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}