Landscape disturbance and change lead to impoverishment and homogenization of butterfly assemblages in Southern Mexico

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León-Cortés, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3848982/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 Mosaics on different land use types characterize current landscapes. This structural complexity is generally made up of areas of native vegetation, vegetation patches in different successional stages, agricultural areas and cattle ranching. In this paper we evaluated the influence of the structure and heterogeneity of fragmented landscapes on butterfly assemblages in a heavily fragmented area in Veracruz State, Mexico. From June to November 2021, ten landscapes were studied and characterized to estimate compositional and configurational heterogeneity, and the type and extent of the landscape elements present. 160 fixed transects were performed to gather measures of butterfly richness, abundance, diversity, dominance and habitat specificity. We recorded 91 species of three families, of which generalist species comprised more than 85% of the total number of species. Compositional heterogeneity and agro-pastoral systems negatively influenced species richness. Likewise, pastures and crops cover had a significant effect on species abundance. Our results showed that butterfly community in present tropical landscapes might be undergoing a process of biotic homogenization, since a vast majority of common and widespread species were dominant and despite an important amount of native vegetation still persisted in some of the study landscapes. Management of the agro-pastoral landscape should include the provision of environments (wooded pastures, remnant treefall areas) that promote the presence of functional habitat for a variety of butterflies, including those with specific habitat requirements. Lepidoptera diversity biodiversity loss habitat fragmentation Figures Figure 1 Figure 2 Figure 3 Introduction In Mexico, 90% of the original cover of tropical forests has been impacted or diminished (Challenger 1988). Currently it is estimated that the occupied area by pristine tropical rain forests encompasses around 12,250 km 2 , which corresponds to 0.6% of the national territory (INEGI 2016 ). Due to an intense process of deforestation associated mainly with livestock, agriculture and the growth of human settlements (Dirzo and García 1992 ; Mendoza and Dirzo 1999 ; Ellis et al. 2017 ), tropical forests outside biosphere reserves are represented by fragments of different sizes and forms and increasingly isolated from each other (Moreno-Sanchez et al. 2011). Landscapes resulting from deforestation processes are generally composed of landscapes with different degrees of heterogeneity, with a less complex vegetation structure and surrounded by areas of different land uses, such as intense agricultural or livestock and high urban pressure. Native flora and fauna communities that inhabit these landscapes also show significant decreases in abundance and diversity, i.e. the case of plants (Wang et al. 2014 ; Pearse et al. 2018 ); vines (Arroyo-Rodríguez and Toledo-Aceves 2009 ); mammals (McCleery et al. 2018 ) and birds (Carrara et al. 2015 ; Morante-Filho et al. 2018 ). To some extent, the degree of landscape heterogeneity can ameliorate the negative effects of land-use intensity and positively influence species persistence, as it might promote a greater number of vegetation patches with varying degrees of disturbance and potentially offering a variety of resources for species persistence (Tscharntke et al. 2012 ). Landscape heterogeneity is generally approached from two perspectives: one that considers the different landscape elements and their extent (compositional heterogeneity), and one other that includes the number, size and arrangement of habitat fragments (configurational heterogeneity; Fahrig et al. 2011 ). An increase in configurational heterogeneity represents more available niches for a variety of organisms. Likewise, an increase in heterogeneity promoted by a fragmented configuration generates a greater edge effect and connectivity that can facilitate population connectivity (Fahrig et al. 2011 ). Empirical evidence indicates a variety of effects from landscape change and fragmentation on the diversity and structure of key indicator groups, such as insects. For instance, lower species richness of dung beetles, bees and ants have been reported in sites with small (Feer and Hingrath 2005; Meneses-Calvillo et al. 2010 ; Sánchez de Jesús et al. 2016 ; Silva et al. 2016 ), as well as isolated tropical forest fragments (Carvalho and Vasconselos 1999; Brühl et al. 2003 ; Filgueiras et al. 2011 ). Weibull et al. ( 2000 ) reported that butterfly species diversity is associated with landscape heterogeneity at a local scale, while abundance is related to a regional scale. González-Estébanez et al. ( 2011 ) pointed out that more heterogeneous landscapes contain higher butterfly species richness, while abundance was higher in landscapes dominated by cereal crops and negatively affected by crop area. Rundlöf and Smith ( 2006 ) indicated that butterfly species richness and abundance increased in more heterogeneous landscapes, while butterflies, dung beetles and ants with specific habitat requirements exhibit a significant population decrease in fragmented sites (Filgueiras et al. 2011 ; Leal et al. 2012 ; Soga and Shinsuke 2013). The generality of these reports suggests that a simplification in the structure of landscapes allow generalist species to thrive more often and potentially shaping species assemblages, a phenomenon described as biotic homogenization (Mckinney and Lockwood 1999 ; Olden et al. 2004 ). For Lepidoptera members, occurring in Holartic environments, biotic homogenization processes have been associated with anthropogenic activities (White and Kerr 2007 ), particularly through agricultural intensification (Ekroos et al. 2010 ), livestock (Börsching et al. 2013), fragmentation of semi-natural grasslands (Eskildsen et al. 2015 ; Valtonen et al. 2017 ) and urbanization (Merckx and Van Dyck 2019 ). In Neotropical environments, where the vast majority of Lepidoptera members occur and where high rates of fragmentation of natural habitats take place, this phenomenon has been poorly addressed. In this paper, we tested the hypothesis of biotic homogenization in a range of landscapes that have undergone severe transformations over the past 50 years: a formerly Mexican tropical rain forest area of Veracruz, where it is estimated that perhaps < 10% of the original tropical forest cover remains (Rzedowski, 1978 ; INEGI, 2009 ), and where mostly remnant fragments include vegetation patches of different successional stages (INEGI, 2009 ), livestock and intensive agriculture. Our objectives were: 1) to assess the diversity and change of butterfly assemblages associated to landscapes with different degrees of fragmentation and heterogeneity; 2) to investigate the habitat association for the recorded butterflies from the study landscapes and 3) to determine the effects of landscape heterogeneity on butterfly species diversity measures. We discuss our findings in light of Neotropical Forest disturbance and resulting butterfly assemblages. Material and Methods Study area The study area is located in the state of Veracruz, Mexico, within the municipalities of Paso de Ovejas and Manlio Fabio Altamirano (Fig. 1 ). It has an elevation ranging from 10 to 400 meters above sea level. The climate is warm and sub-humid with summer rains; main rains occur between June and September with a marked division between the rainy and dry seasons. The average humidity recorded is 61% per year (INEGI 2009 ). Land use comprises mostly land for livestock and intensive agriculture, where the main crops are beans, squash, chili, peanuts and corn. There are also plantations of fruit trees such as papaya, lemon and mango (Gallardo-López et al. 2002 ; Lang-Ovalle et al. 2007 ). Landscape characterization We selected ten landscapes in the study area that included a gradient of land use ranging from very intensive (landscapes with a higher cover of temporary crops with a constant rotation) to low intensity (landscapes with a higher cover of remnant tropical deciduous forest fragments). We estimated landscape heterogeneity of each study landscape by delimiting a buffer radius of 500 m (total area 78.54 ha). We also measured the cover area of different vegetation and land use types (Rzedowsky 1978), using digitized high-resolution satellite images (Google Earth Pro-2018 ver. 7.3.3, 2014). To validate our vegetation and land use classification, we carried out ground-truth surveys. Maps of each landscape were generated using QGIS software ver.3.18.3 (QGIS 2022), see Supplementary Information 1. Butterfly transects In each landscape unit, we established a 500 m fixed transect for butterfly observation and recording. Each transect was walked 16 times, between 10:00 and 16:00 h to ensure the observation of active butterflies (Pollard and Yates 1993 ; Caldas and Robins 2003). Sampling was carried out from June to November 2021, for a total of 160 transects (10 landscapes X 16 transect walks). Butterflies were sighted with the aid of binoculars (VORTEX Crossfire HD 10x42®); those species difficult to identify in flight were collected with an aerial entomological net for identification and subsequently released (De Vries 1987; Llorente-Bousquets et al. 1997 ; Luis-Martínez et al. 2003 ; Glassberg 2017 ). To ensure that members of the family Nymphalidae were adequately recorded, we placed two Van Someren-Rydon traps 200 m apart in each study landscape (Rydon, 1964 ). Each trap was baited using a fruit mixture -previously fermented for two days- that included beer, pineapple ( Ananas comosus ), zapote ( Manilkara zapota ) and nanche ( Byrsonima crassifolia ). Bait trapping has been safely used to monitor changes in butterfly abundances over time, to compare species composition and abundances between sites, and to track the movement of individuals (Hughes et al. 1998 ). The collected specimens were prepared and identified in the entomological collection of the Centro de Agroecología of Instituto de Ciencias, Benemérita Universidad Autónoma de Puebla. Only species belonging to the families Papilionidae, Pieridae and Nymphalidae were registered. These families are well known taxonomically (New 1997 ), and have been the subject of intense sampling, identification and monitoring by us throughout a number of regions in Mexico (Molina-Martínez et al. 2013 ; 2016 ). Butterfly habitat associations We examined the habitat association for the recorded butterflies from different landscapes, based on field observations and on the information given in the species account provided by De Vries (1987), Scott ( 1986 ), and from the information given in the species accounts of Warren et al. ( 2016 , http://www.butterfliesofamerica.com/L/intro.htm ). Butterfly species were classified as: (1) species occupying unmodified vegetation (when no mention was made that they occupy secondary or agricultural habitats), but do make some use of modified habitats, in most cases the latter are used very rarely by these species and usually only when modified and unmodified habitat are adjacent. (2) Species that mainly occupy modified vegetation and open habitats. Thus category 1 was considered to be species with specific habitat requirements or relatively specialist and category 2 were considered to be relatively generalist species. Butterfly sampling completeness We estimate completeness of the butterfly sampling for each landscape based on the coverage of the sample (Chao et al. 2016 ), which considers the proportion of the total number of individuals of the species represented in the sample and has values that range between 0 (minimum completeness) and 1 (maximum completeness; Chao and Jost 2012 ). We estimated sample coverage values for the observed butterfly diversity of each landscape based on the number of individuals. We estimated the number of effective species “ 1 D” (Chao et al. 2014 , Hsieh et al. 2016 ), 1 D values represent the exponential of Shannon index and indicate the values of the relatively abundance species (Jost 2006 ). Sampling completeness was estimated using rarefaction and extrapolation curves (Hsieh et al. 2016 ) Curves were calculated with 1000 randomizations and extrapolated to twice the number of individuals in the assemblage with the lowest sample coverage (Chao and Chiu 2016 ), using the iNEXT software (Hsieh et al. 2016 ). Butterfly assemblages We estimated dominance values for butterfly assemblages in each landscape in two ways: by estimating a Berger-Parker index, which considers the proportion of individuals for the most abundant species in each community, and by building Whittaker dominance curves (Magurran 2004 ). For the latter, the slope value was used as a proxy of the richness and evenness of the sampled assemblages. The species diversity value is higher when the slope of the line approaches zero, while dominance is higher as the slope becomes negative (Caballero and León-Cortés 2014 ). The slope value of the species abundance distribution was compared between landscape sites using Kolmogorov-Smirnov tests (Tokeshi 1993 ), and significance values were rectified using the Bonferroni correction. Landscape heterogeneity effects To estimate landscape heterogeneity values we consider two measures, compositional heterogeneity and configurational heterogeneity. Compositional heterogeneity referred to the number of vegetation cover types, while configurational heterogeneity is associated with the number, size and spatial arrangement of habitat fragments that make up each landscape unit (Duelli 1997 ; Fahrig and Nuttle 2005 ). To estimate compositional heterogeneity values, we calculated a Shannon diversity index based on the relative cover that relates vegetation composition; while configurational heterogeneity was estimated by dividing the sum of the perimeter in meters of all fragment edges by the number of fragments for each landscape unit (Perovic et al. 2015 ). We built General Linear Models to determine the effect of landscape heterogeneity (compositional and configurational heterogeneity, our explanatory variables) on butterfly species richness, relative abundance, species diversity ( 1 D), species dominance, number and abundance of generalists’ and specialists’ species (our response variables). We assumed a Poisson distribution when the response variable exhibited a Poisson distribution and a variance equal to the mean. A Quasi-Poisson distribution was assumed when the response variable showed overdispersion or underdispersion, meaning a variance greater or smaller than the expected mean under a Poisson distribution. We selected the model with the lowest Akaike information criterion (AIC). GLMs were performed in the R environment v 4 .2.0 (R Core Team 2022 ) by applying the function "glm()". Results Butterfly diversity and assemblages A total of 9,466 butterfly individuals belonging to 91 species were recorded, of which 12 belonged to the family Papilionidae, 28 to Pieridae and 51 to Nymphalidae. Two species, Eurema daira eugenia (Wallengren, 1860) and Pyrisitia proterpia (Fabricius, 1775), recorded the highest abundance values (13% and 20% of the total abundance recorded, respectively). Of the 91 species recorded, only 10 (12%) of them were classified as specialists, while 81 (88%) were classified as generalists. Three of the assessed landscapes did not record any specialist, five landscapes recorded four and two landscapes recorded only one. The abundance of specialists per site ranged from one to eight individuals and made up < 1% of the total abundance (Table 1 ). The taxonomic list of species recorded is shown in Supplementary Information 2. Table 1 Landscape heterogeneity and diversity values and richness and abundance for the entire butterfly community and for three butterfly families. Site Con H a Com H b Abu c Rich d S Abu e G Abu f S Rich g G Rich h B-P I i P Rich j P Abu k Pi Ric l Pi Abu m N Rich n N Abu o Crop 1 413.84 1.544 918 54 0 918 0 54 0.16 9 58 21 631 21 183 Crop 2 756.17 1.665 885 44 0 885 0 44 0.25 5 16 21 629 17 214 Pas 1 551.56 1.429 1215 56 6 1209 5 51 0.172 7 21 20 825 26 323 Pas 2 391.36 1.524 898 51 3 895 3 48 0.229 6 24 21 606 23 254 TF 1 670.52 1.064 704 57 4 700 4 53 0.341 10 25 29 457 25 217 TF2 588.98 1.121 841 63 11 830 5 58 0.168 10 27 23 560 27 232 F 1 547.6 1.671 796 53 0 796 0 53 0.184 7 14 23 615 21 147 F 2 580.03 1.734 683 52 5 681 2 50 0.259 9 33 22 342 19 306 IH 1 595.12 1.422 1171 56 0 1171 0 56 0.147 8 34 19 908 28 208 IH 2 275.53 1.212 1355 72 6 1349 5 67 0.237 7 19 28 1005 35 308 a Configurational heterogeneity, b Compositional heterogeneity, c Total Abundance, d Total richness, e Specialist species abundance, f Generalist species abundance, g Specialist species richness, h Generalist species richness, i Berger-Parker Index, j Papilionidae richness, k Papilionidae abundance, l Pieridae richness, m Pieridae abundance, n Nymphalidae richness, o Nymphalidae abundance. Sample coverage curves showed values ranging from 0.93 to 0.97, indicating that sampling effort across all landscape units was adequate (Fig. 2 a). According to species diversity ( 1 D) five landscapes showed higher levels of diversity (Fig. 2 b). Two landscapes had a significant percentage of tree vegetation in their composition. However, the diversity values reported were not significantly different compared to landscapes with open habitats or those containing crops and pastures (Fig. 2 b). Whittaker dominance curves indicated that P. proterpia was the most abundant species in seven of the 10 landscapes, E. daira eugenia was the most abundant in two landscapes, while Anartia fatima fatima (Fabricius 1973) was the most abundant in one. Comparison of the slope of the distribution of species abundance across the landscapes studied showed no significant differences among landscapes (P > 0.05; Fig. 3 ). Landscape heterogeneity effects Configurational heterogeneity values ranged from 275.53 to 756.17 ha for the study landscapes, while compositional heterogeneity showed relatively comparable values, i.e. from 1.0 to 1.7 (Table 1 ; Supplementary Information 3). Generalized linear models indicated a negative effect of compositional heterogeneity and agro-pastoral systems upon the total number of butterflies, generalist butterflies and the total number of Nymphalidae species. Pasture fields and arboreal pasture had a positive effect on the total number of butterflies and pierid abundance. Similarly, pasture fields showed a positive effect on species richness of Nymphalidae members, and temporary crops on the abundance of Papilionidae species (Table 2 ). Table 2 Response of butterfly diversity measures to compositional heterogeneity and landscape elements (results from generalized linear models using function “glm()”, in R see text). Compositional heterogeneity Intercept Slope R 2 Total richness Estimate 98.95844 -0.6695831 0.524 P value < 0.0001 *0.0309 Nymphalidae richness Estimate 55.72896 -0.5533304 0.443 P value < 0.0001 *0.0358 Pasture fields Intercept Slope R 2 Total abundance Estimate 702.6255 1.012483 0.735 P value < 0.0001 *0.00134 Pieridae abundance Estimate 446.9747 1.015941 0.707 P value < 0.0001 *0.00165 Nymphalidae richness Estimate 19.19877 1.009473 0.505 P value < 0.0001 *0.0206 Arboreal pasture Intercept Slope R 2 Total abundance Estimate 752.4993 1.014271 0.701 P value < 0.0001 *0.00246 Pieridae abundance Estimate 508.3194 1.015905 0.511 P value < 0.0001 *0.0183 Agropasture Intercept Slope R 2 Total Richness Estimate 60.55694 -0.9780309 0.595 P value < 0.0001 *0.0103 Generalist species richness Estimate 57.46867 -0.9803849 0.63 P value < 0.0001 *0.048 Nymphalidae Richness Estimate 26.95773 -0.9681193 0.474 P value < 0.0001 *0.0315 Temporary crop Intercept Slope R 2 Papilionidae abundance Estimate 21.39861 1.018138 0.401 P value < 0.0001 *0.035 Discussion Our results indicate that the diversity of butterfly species in the study area did not show significant association with the observed levels of heterogeneity, but instead single landscape elements contributed to butterfly family occupancy and change (see below). Landscapes containing “tree fallow” registered high levels of species diversity, but these levels remain comparable to those landscapes that mainly included open habitats and higher anthropogenic pressure. Our results are in agreement with those reported by Martínez-Sánchez et al. ( 2020 ), whom detected no significant differences in species diversity between landscapes with different vegetation cover and successional vegetation from fragmented tropical rainforest in eastern Mexico (however, see González-Valdivia et al. ( 2016 ), for a contrasting example). In a historical butterfly assessment from the nearest biosphere reserve in Los Tuxtlas ( ca. 150 km to the southeast from our study region; Raguso and Llorente-Bousquets ( 1990 )), authors reported 36 (24%) specialist species within the families Papilionidae, Pieridae and Nymphalidae ( 1 D = 133.55), compared to 10 specialists (12%) recorded by us. Although we must consider the nature and condition of Los Tuxtlas reserve (where a blend of pristine tropical rain forest habitats and disturbed habitats occur), this mixture of habitats might provide refuge and resources for an important number of species with distinct habitat affiliations (Dennis 2010 ). Nonetheless, butterfly assemblages elsewhere from the Tuxtlas reserve (like those recorded in our study landscapes), might be experiencing significant changes in species composition and abundance. The influence of landscape variables on butterfly richness and abundance reported in this study varied. Compositional heterogeneity negatively influenced the overall butterfly richness and particularly upon Nymphalidae members. This may suggest that landscape units with higher compositional heterogeneity are those that are more fragmented and that contained a greater number of open habitats. As the number of disturbed habitats increase, the number of species that are able to tolerate these conditions decreases and only broadly generalist species adapted to open habitats remain (Börschig et al. 2013 ). Open landscapes such as pastures and arboreal pastures had a positive influence on the overall abundance and abundance of the Pieridae family. On the one hand, this can be explained by the fact that the Pieridae was the group that presented a considerably higher abundance compared to Papilionidae and Nymphalidae. In addition, members of Pieridae include species that have been reported to be associated with open environments and pastures (De Vries 1987; Scott 1986 ; Orta et al. 2022 ). Another aspect associated with positive interactions between Pieridae and pastures and arboreal pastures relates to the behavior of these organisms when searching for resources. Species of the genera Pyrisitia and Eurema , tended to concentrate in very large groups around cattle feces. As feces are a considerably abundant resource in pastures, these species are strongly attracted to these areas (DeVries 1987 ). In addition, the presence of isolated flowering shrubs is common in paddocks across our study landscapes. These shrubs are commonly resorted for nectar feeding in considerable abundance by organisms of the subfamily Coliadinae. Therefore, resource availability and the adaptation of pierid members to open sites such as pastures seem to be beneficial (Orta et al. 2022 ). Contrary to other studies of butterflies (Enri et al. 2017 ; Lewthwaite and Mooers 2021) and other taxonomic groups (Filgueiras et al. 2021 ), the agropastoral systems in our study area had a negative influence on species richness (as was the case for Nymphalidae). Given the extensive use of agrochemicals across the region, important micro-habitat conditions of many butterfly species can be altered (Pekin 2013 ), and hence a significant reduction in species richness and abundance of specialist butterflies and perhaps of many other insects (Moranz et al. 2012 ; Muratet and Fountaine 2015; Braak et al. 2018 ). Overall, the dominance of the communities in the evaluated landscapes did not show significant differences, including only three species that were the most abundant in our study landscapes ( P. proterpia, E. daira eugenia, A. fatima fatima ). These species are considered generalists and have been able to exploit a greater amount of available resources (Molina Martínez et al. 2016; Orta et al. 2022 ). In contrast, butterflies with specific habitat requirements are the first to suffer local extinctions in heavily fragmented landscapes (Krauss et al. 2003 ; Brückmann et al. 2010 ), and in the long run, generalists might increase their abundance. In our study landscapes, we detected that index values for community dominance did not vary significantly among landscapes (nor even between those that still retained a fair amount of woody cover i.e., tree fallow). This may be an indication that, even in landscapes that had a relatively more complex vegetation composition, extensive regional fragmentation and habitat loss have a strong effect on the butterfly community composition, again favoring the dominance of generalist species. Similarly, remaining landscapes of arboreal vegetation did not seem to maintain important levels of richness and abundance of specialists to the extent that they can make an important contribution to the entire butterfly community (Shahabuddin and Ponte 2005 ; Barlow et al. 2007 ; Uehara-Prado et al. 2007 ; Uehara-Prado and Freitas 2009 ; Melo et al. 2019 ). The results of the present study indicate that the current structure of tropical landscapes in the study area retains butterfly communities composed mostly by generalist species. Species requiring specific habitat conditions conformed a tiny fraction of the community studied and are mainly associated with remnant fragments of mature vegetation. Landscape-level elements had rather idiosyncratic effects on butterfly diversity measures. We argue that biotic homogenization processes are taking place in many parts of tropical Mexico, such that relatively common and widespread species have become the norm in butterfly assemblages. The tiny little fraction of butterfly specialists reported in this study should warn conservationists about the importance of retaining as much pristine habitat as possible, so insect (and perhaps other invertebrate and vertebrate) assemblages do not significantly alter. Declarations Conflicts of interests The authors have no competing interests to declare that are relevant to the content of this article. Funding UH-H Received a scholarship (contract number 772653) from the Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCYT). Author contributions All authors contributed to the study conception and design. Data collection and Material preparation were performed by Ulises Hernández-Hernández and Arcángel Molina-Martínez, data analysis was performed by Ulises Hernández-Hernández, Arcángel Molina-Martínez and Jorge L. León-Cortés. The first draft of the manuscript was written by Arcángel Molina-Martínez and Jorge L. 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Biodivers Conserv 14:1137–1115. https://doi.org/10.1007/s10531-004-7842-3 Silva RJ, Storck-Tonon D, Vaz-de-Mello FZ (2016) Dung beetle (Coleoptera: Scarabaeinae) persistence in Amazonian Forest fragments and adjacent pastures: biogeographic implications for alpha and beta diversity. J Insect Conserv 20:549–564. https://doi.org/10.1007/s10841-016-9885-7 Soga M, Koike S (2013) Patch isolation only matters for specialist butterflies but patch area affects both specialist and generalist species. J For Res 18(3):270–278. https://doi.org/10.1007/s10310-012-0349-y Tokeshi M (1993) Species abundance patterns and community structure. Adv Ecol Res 24:111–186. https://doi.org/10.1016/S0065-2504(08)60042-2 Tscharntke T, Tylianakis JM, Rand TA et al (2012) Landscape moderation of biodiversity patterns and processes-eight hypotheses. Biol Rev 87(3):661–685. https://doi.org/10.1111/j.1469-185X.2011.00216.x Uehara-Prado M, Freitas AVL (2009) The effect of rainforest fragmentation on species diversity and mimicry ring composition of ithomiine butterflies. Insect Conserv Divers 2(1):23–28 Uehara-Prado M, Brown KS Jr, Freitas AVL (2007) Species richness, composition and abundance of fruit‐feeding butterflies in the Brazilian Atlantic Forest: comparison between a fragmented and a continuous landscape. Glob Ecol Biogeogr 16(1):43–54. https://doi.org/10.1111/j.1752-4598.2008.00025.x Valtonen A, Hirka A, Szőcs L, Ayres MP, Roininen H, Csóka G (2017) Long-term species loss and homogenization of moth communities in Central Europe. J Anim Ecol 86:730–738. https://doi.org/10.1111/1365-2656.12687 Wang G, Zuo J, Li X, Liu Y, Yu J, Shao H, Li Y (2014) Low plant diversity and floristic homogenization in fast-urbanizing towns in Shandong Peninsular, China: Effects of urban greening at regional scale for ecological engineering. Ecol Eng 64:179–185. https://doi.org/10.1016/j.ecoleng.2013.12.054 Warren AD, Davis KJ, Stangeland EM, Pelham JP, Willmott KR, Grishin NV (2016) Illustrated list of American butterflies. https://www.butterfliesofamerica.com/L/All.htm . Accessed 14 November 2022 Weibull AC, Bengtsson J, Nohlgren E (2000) Diversity of butterflies in the agricultural landscape: the role of farming system and landscape heterogeneity. Ecography 23(6):743–750. https://doi.org/10.1111/j.1600-0587.2000.tb00317.x White PJ, Kerr JT (2007) Human impacts on environment–diversity relationships: evidence for biotic homogenization from butterfly species richness patterns. Glob Ecol Biogeogr 16:290–299. https://doi.org/10.1111/j.1466-8238.2007.00298.x Unsectioned Paragraphs Tables Section Ecology, Behavior and Bionomics a Configurational heterogeneity, b Compositional heterogeneity, c Total Abundance, d Total richness, e Specialist species abundance, f Generalist species abundance, g Specialist species richness, h Generalist species richness, i Berger-Parker Index, j Papilionidae richness, k Papilionidae abundance, l Pieridae richness, m Pieridae abundance, n Nymphalidae richness, o Nymphalidae abundance. Supplementary Files SupplementaryInformation1UHHetal2024.pdf SupplementaryInformation2UHHetal2024.pdf SupplementaryInformation3UHHetal2024.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. 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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-3848982","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267060106,"identity":"5b450907-5c7d-4dec-accf-452c811afb2a","order_by":0,"name":"Ulises Hernández-Hernández","email":"","orcid":"","institution":"Benemérita Universidad Autónoma de Puebla: Benemerita Universidad Autonoma de Puebla","correspondingAuthor":false,"prefix":"","firstName":"Ulises","middleName":"","lastName":"Hernández-Hernández","suffix":""},{"id":267060107,"identity":"467caaa2-5fe8-4b3e-8335-5c7eb3b5f003","order_by":1,"name":"Arcángel Molina-Martínez","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-5424-3997","institution":"Benemérita Universidad Autónoma de Puebla: Benemerita Universidad Autonoma de Puebla","correspondingAuthor":true,"prefix":"","firstName":"Arcángel","middleName":"","lastName":"Molina-Martínez","suffix":""},{"id":267060108,"identity":"3d6b9522-ecc7-402a-b1a7-83b1c734fee9","order_by":2,"name":"Jorge L. León-Cortés","email":"","orcid":"","institution":"El Colegio De La Frontera Sur: El Colegio de la Frontera Sur","correspondingAuthor":false,"prefix":"","firstName":"Jorge","middleName":"L.","lastName":"León-Cortés","suffix":""},{"id":267060109,"identity":"c1eb0077-8ddd-408b-822a-0c923404c4a2","order_by":3,"name":"César A. Sandoval-Ruiz","email":"","orcid":"","institution":"Benemérita Universidad Autónoma de Puebla: Benemerita Universidad Autonoma de Puebla","correspondingAuthor":false,"prefix":"","firstName":"César","middleName":"A.","lastName":"Sandoval-Ruiz","suffix":""}],"badges":[],"createdAt":"2024-01-09 18:31:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3848982/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3848982/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49712826,"identity":"1b4c1559-67b1-4b44-bc7d-d4fff85e16a1","added_by":"auto","created_at":"2024-01-16 20:32:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":868181,"visible":true,"origin":"","legend":"\u003cp\u003eThe location of the study area showing the sampling sites in southern Veracruz, México (black circles)\u003c/p\u003e","description":"","filename":"Figure1UHHetal2024.png","url":"https://assets-eu.researchsquare.com/files/rs-3848982/v1/c207d9926e9cf117745daaf6.png"},{"id":49712827,"identity":"0f4683c9-de12-4587-9779-4ab2add76ae8","added_by":"auto","created_at":"2024-01-16 20:32:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":309393,"visible":true,"origin":"","legend":"\u003cp\u003ea) Sample coverage curve for the evaluated landscapes. b) Individual-based rarefaction for \u003csup\u003e1\u003c/sup\u003eD values. TF: Tree Fallow; C: Crop; F: Fruit; IH Intermediate heterogeneity; P: Pasture. Solid lines indicates interpolation and dotted lines extrapolation values\u003c/p\u003e","description":"","filename":"Figure2UHHetal2024.png","url":"https://assets-eu.researchsquare.com/files/rs-3848982/v1/fc8c8450d2f2c5d90a4c6c84.png"},{"id":49713422,"identity":"505c866a-922b-4257-8ca0-65c42cd01189","added_by":"auto","created_at":"2024-01-16 20:40:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":787464,"visible":true,"origin":"","legend":"\u003cp\u003eAbundance range curves for each survey site in the study area. a) Crop 1; b) Crop 2; c) Pasture 1; d) Pasture 2; e) Tree Fall 1; f) Tree Fall 2; g) Fruit 1; h) Fruit 2; i) Intermediate Heterogeneity 1; j) Intermediate Heterogeneity 2\u003c/p\u003e","description":"","filename":"Figure3UHHetal2024.png","url":"https://assets-eu.researchsquare.com/files/rs-3848982/v1/e7a0e468d5c4053c1b76e572.png"},{"id":54633036,"identity":"87c58325-257c-451b-b217-2560c6002902","added_by":"auto","created_at":"2024-04-13 20:28:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1233642,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3848982/v1/76f0496c-924f-4a26-a2f0-298021f9f4c4.pdf"},{"id":49712830,"identity":"b46e21a4-672d-4f05-a71d-230365cf3e0f","added_by":"auto","created_at":"2024-01-16 20:32:57","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":164974,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation1UHHetal2024.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3848982/v1/1b430ef8f31cb040f9ce47e8.pdf"},{"id":49712829,"identity":"810950aa-1d3a-4909-a756-913b392d9e19","added_by":"auto","created_at":"2024-01-16 20:32:57","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":216774,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation2UHHetal2024.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3848982/v1/baf0530f68e467318ea672f5.pdf"},{"id":49712831,"identity":"ef31f992-a548-4a6c-bc0e-4149e4b4b7cb","added_by":"auto","created_at":"2024-01-16 20:32:57","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":1110114,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation3UHHetal2024.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3848982/v1/2abe9c3e9e6b7911d2d21777.pdf"}],"financialInterests":"","formattedTitle":"Landscape disturbance and change lead to impoverishment and homogenization of butterfly assemblages in Southern Mexico","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn Mexico, 90% of the original cover of tropical forests has been impacted or diminished (Challenger 1988). Currently it is estimated that the occupied area by pristine tropical rain forests encompasses around 12,250 km\u003csup\u003e2\u003c/sup\u003e, which corresponds to 0.6% of the national territory (INEGI \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Due to an intense process of deforestation associated mainly with livestock, agriculture and the growth of human settlements (Dirzo and Garc\u0026iacute;a \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Mendoza and Dirzo \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Ellis et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), tropical forests outside biosphere reserves are represented by fragments of different sizes and forms and increasingly isolated from each other (Moreno-Sanchez et al. 2011).\u003c/p\u003e \u003cp\u003eLandscapes resulting from deforestation processes are generally composed of landscapes with different degrees of heterogeneity, with a less complex vegetation structure and surrounded by areas of different land uses, such as intense agricultural or livestock and high urban pressure. Native flora and fauna communities that inhabit these landscapes also show significant decreases in abundance and diversity, i.e. the case of plants (Wang et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pearse et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); vines (Arroyo-Rodr\u0026iacute;guez and Toledo-Aceves \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e); mammals (McCleery et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and birds (Carrara et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Morante-Filho et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To some extent, the degree of landscape heterogeneity can ameliorate the negative effects of land-use intensity and positively influence species persistence, as it might promote a greater number of vegetation patches with varying degrees of disturbance and potentially offering a variety of resources for species persistence (Tscharntke et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLandscape heterogeneity is generally approached from two perspectives: one that considers the different landscape elements and their extent (compositional heterogeneity), and one other that includes the number, size and arrangement of habitat fragments (configurational heterogeneity; Fahrig et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). An increase in configurational heterogeneity represents more available niches for a variety of organisms. Likewise, an increase in heterogeneity promoted by a fragmented configuration generates a greater edge effect and connectivity that can facilitate population connectivity (Fahrig et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEmpirical evidence indicates a variety of effects from landscape change and fragmentation on the diversity and structure of key indicator groups, such as insects. For instance, lower species richness of dung beetles, bees and ants have been reported in sites with small (Feer and Hingrath 2005; Meneses-Calvillo et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; S\u0026aacute;nchez de Jes\u0026uacute;s et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Silva et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), as well as isolated tropical forest fragments (Carvalho and Vasconselos 1999; Br\u0026uuml;hl et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Filgueiras et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Weibull et al. (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) reported that butterfly species diversity is associated with landscape heterogeneity at a local scale, while abundance is related to a regional scale. Gonz\u0026aacute;lez-Est\u0026eacute;banez et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) pointed out that more heterogeneous landscapes contain higher butterfly species richness, while abundance was higher in landscapes dominated by cereal crops and negatively affected by crop area. Rundl\u0026ouml;f and Smith (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) indicated that butterfly species richness and abundance increased in more heterogeneous landscapes, while butterflies, dung beetles and ants with specific habitat requirements exhibit a significant population decrease in fragmented sites (Filgueiras et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Leal et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Soga and Shinsuke 2013).\u003c/p\u003e \u003cp\u003eThe generality of these reports suggests that a simplification in the structure of landscapes allow generalist species to thrive more often and potentially shaping species assemblages, a phenomenon described as biotic homogenization (Mckinney and Lockwood \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Olden et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). For Lepidoptera members, occurring in Holartic environments, biotic homogenization processes have been associated with anthropogenic activities (White and Kerr \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), particularly through agricultural intensification (Ekroos et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), livestock (B\u0026ouml;rsching et al. 2013), fragmentation of semi-natural grasslands (Eskildsen et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Valtonen et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and urbanization (Merckx and Van Dyck \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In Neotropical environments, where the vast majority of Lepidoptera members occur and where high rates of fragmentation of natural habitats take place, this phenomenon has been poorly addressed.\u003c/p\u003e \u003cp\u003eIn this paper, we tested the hypothesis of biotic homogenization in a range of landscapes that have undergone severe transformations over the past 50 years: a formerly Mexican tropical rain forest area of Veracruz, where it is estimated that perhaps\u0026thinsp;\u0026lt;\u0026thinsp;10% of the original tropical forest cover remains (Rzedowski, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; INEGI, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and where mostly remnant fragments include vegetation patches of different successional stages (INEGI, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), livestock and intensive agriculture. Our objectives were: 1) to assess the diversity and change of butterfly assemblages associated to landscapes with different degrees of fragmentation and heterogeneity; 2) to investigate the habitat association for the recorded butterflies from the study landscapes and 3) to determine the effects of landscape heterogeneity on butterfly species diversity measures. We discuss our findings in light of Neotropical Forest disturbance and resulting butterfly assemblages.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe study area is located in the state of Veracruz, Mexico, within the municipalities of Paso de Ovejas and Manlio Fabio Altamirano (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It has an elevation ranging from 10 to 400 meters above sea level. The climate is warm and sub-humid with summer rains; main rains occur between June and September with a marked division between the rainy and dry seasons. The average humidity recorded is 61% per year (INEGI \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Land use comprises mostly land for livestock and intensive agriculture, where the main crops are beans, squash, chili, peanuts and corn. There are also plantations of fruit trees such as papaya, lemon and mango (Gallardo-L\u0026oacute;pez et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Lang-Ovalle et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eLandscape characterization\u003c/h2\u003e \u003cp\u003eWe selected ten landscapes in the study area that included a gradient of land use ranging from very intensive (landscapes with a higher cover of temporary crops with a constant rotation) to low intensity (landscapes with a higher cover of remnant tropical deciduous forest fragments). We estimated landscape heterogeneity of each study landscape by delimiting a buffer radius of 500 m (total area 78.54 ha). We also measured the cover area of different vegetation and land use types (Rzedowsky 1978), using digitized high-resolution satellite images (Google Earth Pro-2018 ver. 7.3.3, 2014). To validate our vegetation and land use classification, we carried out ground-truth surveys. Maps of each landscape were generated using QGIS software ver.3.18.3 (QGIS 2022), see Supplementary Information 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eButterfly transects\u003c/h2\u003e \u003cp\u003eIn each landscape unit, we established a 500 m fixed transect for butterfly observation and recording. Each transect was walked 16 times, between 10:00 and 16:00 h to ensure the observation of active butterflies (Pollard and Yates \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Caldas and Robins 2003). Sampling was carried out from June to November 2021, for a total of 160 transects (10 landscapes X 16 transect walks). Butterflies were sighted with the aid of binoculars (VORTEX Crossfire HD 10x42\u0026reg;); those species difficult to identify in flight were collected with an aerial entomological net for identification and subsequently released (De Vries 1987; Llorente-Bousquets et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Luis-Mart\u0026iacute;nez et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Glassberg \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). To ensure that members of the family Nymphalidae were adequately recorded, we placed two Van Someren-Rydon traps 200 m apart in each study landscape (Rydon, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1964\u003c/span\u003e). Each trap was baited using a fruit mixture -previously fermented for two days- that included beer, pineapple (\u003cem\u003eAnanas comosus\u003c/em\u003e), zapote (\u003cem\u003eManilkara zapota\u003c/em\u003e) and nanche (\u003cem\u003eByrsonima crassifolia\u003c/em\u003e). Bait trapping has been safely used to monitor changes in butterfly abundances over time, to compare species composition and abundances between sites, and to track the movement of individuals (Hughes et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The collected specimens were prepared and identified in the entomological collection of the Centro de Agroecolog\u0026iacute;a of Instituto de Ciencias, Benem\u0026eacute;rita Universidad Aut\u0026oacute;noma de Puebla. Only species belonging to the families Papilionidae, Pieridae and Nymphalidae were registered. These families are well known taxonomically (New \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), and have been the subject of intense sampling, identification and monitoring by us throughout a number of regions in Mexico (Molina-Mart\u0026iacute;nez et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eButterfly habitat associations\u003c/h2\u003e \u003cp\u003eWe examined the habitat association for the recorded butterflies from different landscapes, based on field observations and on the information given in the species account provided by De Vries (1987), Scott (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), and from the information given in the species accounts of Warren et al. (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.butterfliesofamerica.com/L/intro.htm\u003c/span\u003e\u003cspan address=\"http://www.butterfliesofamerica.com/L/intro.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Butterfly species were classified as: (1) species occupying unmodified vegetation (when no mention was made that they occupy secondary or agricultural habitats), but do make some use of modified habitats, in most cases the latter are used very rarely by these species and usually only when modified and unmodified habitat are adjacent. (2) Species that mainly occupy modified vegetation and open habitats. Thus category 1 was considered to be species with specific habitat requirements or relatively specialist and category 2 were considered to be relatively generalist species.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eButterfly sampling completeness\u003c/h2\u003e \u003cp\u003eWe estimate completeness of the butterfly sampling for each landscape based on the coverage of the sample (Chao et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which considers the proportion of the total number of individuals of the species represented in the sample and has values that range between 0 (minimum completeness) and 1 (maximum completeness; Chao and Jost \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). We estimated sample coverage values for the observed butterfly diversity of each landscape based on the number of individuals. We estimated the number of effective species \u0026ldquo;\u003csup\u003e1\u003c/sup\u003eD\u0026rdquo; (Chao et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Hsieh et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), \u003csup\u003e1\u003c/sup\u003eD values represent the exponential of Shannon index and indicate the values of the relatively abundance species (Jost \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Sampling completeness was estimated using rarefaction and extrapolation curves (Hsieh et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) Curves were calculated with 1000 randomizations and extrapolated to twice the number of individuals in the assemblage with the lowest sample coverage (Chao and Chiu \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), using the iNEXT software (Hsieh et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eButterfly assemblages\u003c/h2\u003e \u003cp\u003eWe estimated dominance values for butterfly assemblages in each landscape in two ways: by estimating a Berger-Parker index, which considers the proportion of individuals for the most abundant species in each community, and by building Whittaker dominance curves (Magurran \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). For the latter, the slope value was used as a proxy of the richness and evenness of the sampled assemblages. The species diversity value is higher when the slope of the line approaches zero, while dominance is higher as the slope becomes negative (Caballero and Le\u0026oacute;n-Cort\u0026eacute;s \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The slope value of the species abundance distribution was compared between landscape sites using Kolmogorov-Smirnov tests (Tokeshi \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), and significance values were rectified using the Bonferroni correction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eLandscape heterogeneity effects\u003c/h2\u003e \u003cp\u003eTo estimate landscape heterogeneity values we consider two measures, compositional heterogeneity and configurational heterogeneity. Compositional heterogeneity referred to the number of vegetation cover types, while configurational heterogeneity is associated with the number, size and spatial arrangement of habitat fragments that make up each landscape unit (Duelli \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Fahrig and Nuttle \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). To estimate compositional heterogeneity values, we calculated a Shannon diversity index based on the relative cover that relates vegetation composition; while configurational heterogeneity was estimated by dividing the sum of the perimeter in meters of all fragment edges by the number of fragments for each landscape unit (Perovic et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe built General Linear Models to determine the effect of landscape heterogeneity (compositional and configurational heterogeneity, our explanatory variables) on butterfly species richness, relative abundance, species diversity (\u003csup\u003e1\u003c/sup\u003eD), species dominance, number and abundance of generalists\u0026rsquo; and specialists\u0026rsquo; species (our response variables). We assumed a Poisson distribution when the response variable exhibited a Poisson distribution and a variance equal to the mean. A Quasi-Poisson distribution was assumed when the response variable showed overdispersion or underdispersion, meaning a variance greater or smaller than the expected mean under a Poisson distribution. We selected the model with the lowest Akaike information criterion (AIC). GLMs were performed in the R environment v 4 .2.0 (R Core Team \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) by applying the function \"glm()\".\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eButterfly diversity and assemblages\u003c/h2\u003e \u003cp\u003eA total of 9,466 butterfly individuals belonging to 91 species were recorded, of which 12 belonged to the family Papilionidae, 28 to Pieridae and 51 to Nymphalidae. Two species, \u003cem\u003eEurema daira eugenia\u003c/em\u003e (Wallengren, 1860) and \u003cem\u003ePyrisitia proterpia\u003c/em\u003e (Fabricius, 1775), recorded the highest abundance values (13% and 20% of the total abundance recorded, respectively). Of the 91 species recorded, only 10 (12%) of them were classified as specialists, while 81 (88%) were classified as generalists. Three of the assessed landscapes did not record any specialist, five landscapes recorded four and two landscapes recorded only one. The abundance of specialists per site ranged from one to eight individuals and made up \u0026lt;\u0026thinsp;1% of the total abundance (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The taxonomic list of species recorded is shown in Supplementary Information 2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLandscape heterogeneity and diversity values and richness and abundance for the entire butterfly community and for three butterfly families.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"16\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" 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Abu\u003csup\u003em\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eN Rich\u003csup\u003en\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eN Abu\u003csup\u003eo\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrop 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e413.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrop 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e756.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePas 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e551.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e323\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePas 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e391.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTF 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e670.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e588.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e547.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e580.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e306\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIH 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e595.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIH 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e275.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"16\"\u003e\u003csup\u003ea\u003c/sup\u003e Configurational heterogeneity, \u003csup\u003eb\u003c/sup\u003e Compositional heterogeneity, \u003csup\u003ec\u003c/sup\u003e Total Abundance, \u003csup\u003ed\u003c/sup\u003e Total richness, \u003csup\u003ee\u003c/sup\u003e Specialist species abundance, \u003csup\u003ef\u003c/sup\u003e Generalist species abundance, \u003csup\u003eg\u003c/sup\u003e Specialist species richness, \u003csup\u003eh\u003c/sup\u003e Generalist species richness, \u003csup\u003ei\u003c/sup\u003e Berger-Parker Index, \u003csup\u003ej\u003c/sup\u003e Papilionidae richness, \u003csup\u003ek\u003c/sup\u003e Papilionidae abundance, \u003csup\u003el\u003c/sup\u003e Pieridae richness, \u003csup\u003em\u003c/sup\u003e Pieridae abundance, \u003csup\u003en\u003c/sup\u003e Nymphalidae richness, \u003csup\u003eo\u003c/sup\u003e Nymphalidae abundance.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSample coverage curves showed values ranging from 0.93 to 0.97, indicating that sampling effort across all landscape units was adequate (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). According to species diversity (\u003csup\u003e1\u003c/sup\u003eD) five landscapes showed higher levels of diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Two landscapes had a significant percentage of tree vegetation in their composition. However, the diversity values reported were not significantly different compared to landscapes with open habitats or those containing crops and pastures (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Whittaker dominance curves indicated that \u003cem\u003eP. proterpia\u003c/em\u003e was the most abundant species in seven of the 10 landscapes, \u003cem\u003eE. daira eugenia\u003c/em\u003e was the most abundant in two landscapes, while \u003cem\u003eAnartia fatima fatima\u003c/em\u003e (Fabricius 1973) was the most abundant in one. Comparison of the slope of the distribution of species abundance across the landscapes studied showed no significant differences among landscapes (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLandscape heterogeneity effects\u003c/h2\u003e \u003cp\u003eConfigurational heterogeneity values ranged from 275.53 to 756.17 ha for the study landscapes, while compositional heterogeneity showed relatively comparable values, i.e. from 1.0 to 1.7 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary Information 3). Generalized linear models indicated a negative effect of compositional heterogeneity and agro-pastoral systems upon the total number of butterflies, generalist butterflies and the total number of Nymphalidae species. Pasture fields and arboreal pasture had a positive effect on the total number of butterflies and pierid abundance. Similarly, pasture fields showed a positive effect on species richness of Nymphalidae members, and temporary crops on the abundance of Papilionidae species (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResponse of butterfly diversity measures to compositional heterogeneity and landscape elements (results from generalized linear models using function \u0026ldquo;glm()\u0026rdquo;, in R see text).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCompositional heterogeneity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal richness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.95844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.6695831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*0.0309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNymphalidae richness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.72896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.5533304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*0.0358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003ePasture fields\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal abundance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e702.6255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.012483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*0.00134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePieridae abundance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e446.9747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.015941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*0.00165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNymphalidae richness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.19877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.009473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*0.0206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eArboreal pasture\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal abundance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e752.4993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.014271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*0.00246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePieridae abundance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e508.3194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.015905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*0.0183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eAgropasture\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Richness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.55694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.9780309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*0.0103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneralist species richness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.46867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.9803849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNymphalidae Richness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.95773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.9681193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*0.0315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eTemporary crop\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePapilionidae abundance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.39861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.018138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur results indicate that the diversity of butterfly species in the study area did not show significant association with the observed levels of heterogeneity, but instead single landscape elements contributed to butterfly family occupancy and change (see below). Landscapes containing \u0026ldquo;tree fallow\u0026rdquo; registered high levels of species diversity, but these levels remain comparable to those landscapes that mainly included open habitats and higher anthropogenic pressure. Our results are in agreement with those reported by Mart\u0026iacute;nez-S\u0026aacute;nchez et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), whom detected no significant differences in species diversity between landscapes with different vegetation cover and successional vegetation from fragmented tropical rainforest in eastern Mexico (however, see Gonz\u0026aacute;lez-Valdivia et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), for a contrasting example).\u003c/p\u003e \u003cp\u003eIn a historical butterfly assessment from the nearest biosphere reserve in Los Tuxtlas (\u003cem\u003eca.\u003c/em\u003e 150 km to the southeast from our study region; Raguso and Llorente-Bousquets (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1990\u003c/span\u003e)), authors reported 36 (24%) specialist species within the families Papilionidae, Pieridae and Nymphalidae (\u003csup\u003e1\u003c/sup\u003eD =\u0026thinsp;133.55), compared to 10 specialists (12%) recorded by us. Although we must consider the nature and condition of Los Tuxtlas reserve (where a blend of pristine tropical rain forest habitats and disturbed habitats occur), this mixture of habitats might provide refuge and resources for an important number of species with distinct habitat affiliations (Dennis \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Nonetheless, butterfly assemblages elsewhere from the Tuxtlas reserve (like those recorded in our study landscapes), might be experiencing significant changes in species composition and abundance.\u003c/p\u003e \u003cp\u003eThe influence of landscape variables on butterfly richness and abundance reported in this study varied. Compositional heterogeneity negatively influenced the overall butterfly richness and particularly upon Nymphalidae members. This may suggest that landscape units with higher compositional heterogeneity are those that are more fragmented and that contained a greater number of open habitats. As the number of disturbed habitats increase, the number of species that are able to tolerate these conditions decreases and only broadly generalist species adapted to open habitats remain (B\u0026ouml;rschig et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Open landscapes such as pastures and arboreal pastures had a positive influence on the overall abundance and abundance of the Pieridae family. On the one hand, this can be explained by the fact that the Pieridae was the group that presented a considerably higher abundance compared to Papilionidae and Nymphalidae. In addition, members of Pieridae include species that have been reported to be associated with open environments and pastures (De Vries 1987; Scott \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Orta et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Another aspect associated with positive interactions between Pieridae and pastures and arboreal pastures relates to the behavior of these organisms when searching for resources. Species of the genera \u003cem\u003ePyrisitia\u003c/em\u003e and \u003cem\u003eEurema\u003c/em\u003e, tended to concentrate in very large groups around cattle feces. As feces are a considerably abundant resource in pastures, these species are strongly attracted to these areas (DeVries \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). In addition, the presence of isolated flowering shrubs is common in paddocks across our study landscapes. These shrubs are commonly resorted for nectar feeding in considerable abundance by organisms of the subfamily Coliadinae. Therefore, resource availability and the adaptation of pierid members to open sites such as pastures seem to be beneficial (Orta et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Contrary to other studies of butterflies (Enri et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lewthwaite and Mooers 2021) and other taxonomic groups (Filgueiras et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the agropastoral systems in our study area had a negative influence on species richness (as was the case for Nymphalidae). Given the extensive use of agrochemicals across the region, important micro-habitat conditions of many butterfly species can be altered (Pekin \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and hence a significant reduction in species richness and abundance of specialist butterflies and perhaps of many other insects (Moranz et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Muratet and Fountaine 2015; Braak et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, the dominance of the communities in the evaluated landscapes did not show significant differences, including only three species that were the most abundant in our study landscapes (\u003cem\u003eP. proterpia, E. daira eugenia, A. fatima fatima\u003c/em\u003e). These species are considered generalists and have been able to exploit a greater amount of available resources (Molina Mart\u0026iacute;nez et al. 2016; Orta et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In contrast, butterflies with specific habitat requirements are the first to suffer local extinctions in heavily fragmented landscapes (Krauss et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Br\u0026uuml;ckmann et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and in the long run, generalists might increase their abundance. In our study landscapes, we detected that index values for community dominance did not vary significantly among landscapes (nor even between those that still retained a fair amount of woody cover i.e., tree fallow). This may be an indication that, even in landscapes that had a relatively more complex vegetation composition, extensive regional fragmentation and habitat loss have a strong effect on the butterfly community composition, again favoring the dominance of generalist species. Similarly, remaining landscapes of arboreal vegetation did not seem to maintain important levels of richness and abundance of specialists to the extent that they can make an important contribution to the entire butterfly community (Shahabuddin and Ponte \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Barlow et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Uehara-Prado et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Uehara-Prado and Freitas \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Melo et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results of the present study indicate that the current structure of tropical landscapes in the study area retains butterfly communities composed mostly by generalist species. Species requiring specific habitat conditions conformed a tiny fraction of the community studied and are mainly associated with remnant fragments of mature vegetation. Landscape-level elements had rather idiosyncratic effects on butterfly diversity measures. We argue that biotic homogenization processes are taking place in many parts of tropical Mexico, such that relatively common and widespread species have become the norm in butterfly assemblages. The tiny little fraction of butterfly specialists reported in this study should warn conservationists about the importance of retaining as much pristine habitat as possible, so insect (and perhaps other invertebrate and vertebrate) assemblages do not significantly alter.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of interests\u003c/h2\u003e \u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eUH-H Received a scholarship (contract number 772653) from the Consejo Nacional de Humanidades, Ciencias y Tecnolog\u0026iacute;as (CONAHCYT).\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eAll authors contributed to the study conception and design. Data collection and Material preparation were performed by Ulises Hern\u0026aacute;ndez-Hern\u0026aacute;ndez and Arc\u0026aacute;ngel Molina-Mart\u0026iacute;nez, data analysis was performed by Ulises Hern\u0026aacute;ndez-Hern\u0026aacute;ndez, Arc\u0026aacute;ngel Molina-Mart\u0026iacute;nez and Jorge L. Le\u0026oacute;n-Cort\u0026eacute;s. The first draft of the manuscript was written by Arc\u0026aacute;ngel Molina-Mart\u0026iacute;nez and Jorge L. Le\u0026oacute;n-Cort\u0026eacute;s, all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eI. Hern\u0026aacute;ndez-Colorado and G. Rodr\u0026iacute;guez assisted in the field. We thank the local communities of Bandera de Ju\u0026aacute;rez, Paso Moral, El Pozo de Mata Ram\u0026iacute;rez, Paso Panal, Las Trancas and Santa Mar\u0026iacute;a, for their permission to survey butterfly populations. UH-H was supported by a scholarship (contract number 772653) from the Consejo Nacional de Humanidades, Ciencias y Tecnolog\u0026iacute;as (CONAHCYT).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArroyo-Rodr\u0026iacute;guez V, Toledo-Aceves T (2009) Impact of landscape spatial pattern on liana communities in tropical rainforests at Los Tuxtlas Mexico. Appl Veg Sci 12:340\u0026ndash;349. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1654-109X.2009.01030.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1654-109X.2009.01030.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarlow J, Overal WL, Araujo IS, Gardner TA, Peres CA (2007) The value of primary, secondary and plantation forests for fruit-feeding butterflies in the Brazilian Amazon. 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Glob Ecol Biogeogr 16:290\u0026ndash;299. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1466-8238.2007.00298.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1466-8238.2007.00298.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Unsectioned Paragraphs","content":"\u003cp\u003eTables\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSection\u003c/strong\u003e \u003cp\u003eEcology, Behavior and Bionomics\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Configurational heterogeneity, \u003csup\u003eb\u003c/sup\u003e Compositional heterogeneity, \u003csup\u003ec\u003c/sup\u003e Total Abundance, \u003csup\u003ed\u003c/sup\u003e Total richness, \u003csup\u003ee\u003c/sup\u003e Specialist species abundance, \u003csup\u003ef\u003c/sup\u003e Generalist species abundance, \u003csup\u003eg\u003c/sup\u003e Specialist species richness, \u003csup\u003eh\u003c/sup\u003e Generalist species richness, \u003csup\u003ei\u003c/sup\u003e Berger-Parker Index, \u003csup\u003ej\u003c/sup\u003e Papilionidae richness, \u003csup\u003ek\u003c/sup\u003e Papilionidae abundance, \u003csup\u003el\u003c/sup\u003e Pieridae richness, \u003csup\u003em\u003c/sup\u003e Pieridae abundance, \u003csup\u003en\u003c/sup\u003e Nymphalidae richness, \u003csup\u003eo\u003c/sup\u003e Nymphalidae abundance.\u003c/p\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":"Lepidoptera, diversity, biodiversity loss, habitat fragmentation","lastPublishedDoi":"10.21203/rs.3.rs-3848982/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3848982/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMosaics on different land use types characterize current landscapes. This structural complexity is generally made up of areas of native vegetation, vegetation patches in different successional stages, agricultural areas and cattle ranching. In this paper we evaluated the influence of the structure and heterogeneity of fragmented landscapes on butterfly assemblages in a heavily fragmented area in Veracruz State, Mexico. From June to November 2021, ten landscapes were studied and characterized to estimate compositional and configurational heterogeneity, and the type and extent of the landscape elements present. 160 fixed transects were performed to gather measures of butterfly richness, abundance, diversity, dominance and habitat specificity. We recorded 91 species of three families, of which generalist species comprised more than 85% of the total number of species. Compositional heterogeneity and agro-pastoral systems negatively influenced species richness. Likewise, pastures and crops cover had a significant effect on species abundance. Our results showed that butterfly community in present tropical landscapes might be undergoing a process of biotic homogenization, since a vast majority of common and widespread species were dominant and despite an important amount of native vegetation still persisted in some of the study landscapes. Management of the agro-pastoral landscape should include the provision of environments (wooded pastures, remnant treefall areas) that promote the presence of functional habitat for a variety of butterflies, including those with specific habitat requirements.\u003c/p\u003e","manuscriptTitle":"Landscape disturbance and change lead to impoverishment and homogenization of butterfly assemblages in Southern Mexico","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-16 20:32:52","doi":"10.21203/rs.3.rs-3848982/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":"e68ac502-f39d-4327-ad7f-8335434ad3ee","owner":[],"postedDate":"January 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-13T20:20:06+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-16 20:32:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3848982","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3848982","identity":"rs-3848982","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}