Bird assemblages in specialty coffee production landscapes in pre-montane humid subtropical forests

Bird assemblages in specialty coffee production landscapes in pre-montane humid subtropical forests | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bird assemblages in specialty coffee production landscapes in pre-montane humid subtropical forests Nayra Antezana-Alvarado, Gabriel G. Torrico, Luis F. Pacheco, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4825928/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 Coffee-related agricultural intensification affects bird species abundance, richness, and composition through habitat loss and degradation. Production of specialty coffee is expected to be more sustainable and environmentally friendly than conventional coffee. Nevertheless, not all specialty coffee is grown sustainably. To evaluate environmental sustainability, we evaluated the composition of bird assemblages in six specialty coffee-producing communities in Bolivia’s pre-montane subtropical humid forest region. To do this, we measured the diversity, richness, generalist and specialist species, and the effect of habitat on bird assemblages, comparing coffee plots and secondary forest plots as part of “Nature’s matrix”. We found significant differences in the abundance of generalist bird species. We did not find differences in the richness and diversity of specialist species. Plant strata, herbaceous leaf cover, and shrub leaf cover affected the assemblages of generalist species. Our results represent a first step toward understanding the intricate relationship between biodiversity and specialty coffee production, highlighting the importance of considering regional differences in landscape characteristics – conceived of as Nature’s matrix – when examining biodiversity in specialty coffee systems. Bird assemblages Nature’s matrix specialty coffee sustainability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Land use practices can have varying impacts on local plant and animal species (Gil-Tena et al. 2009; Newbold et al. 2013; Prokopová et al. 2019; Schürings et al. 2022). One possible impact is biotic homogenization, meaning a process of increasing species invasions and extinctions resulting in greater taxonomic, genetic, or functional similarity between two or more biotas over time (Olden and Rooney 2006 ). Agroforestry and its associated management practices have been identified as potential means of promoting biodiversity (Marconi and Armengot 2020 ). Of special relevance here is the theoretical framework proposed by Perfecto et al. ( 2019 ), in particular the concept of “Nature’s matrix” emphasizing that agriculture is part of a broader ecological matrix. This means that natural habitats, such as forests and rivers, interact with and impact agricultural areas. In this way, studying production systems using a Nature’s matrix-approach can improve our understanding of the contribution of biodiversity to sustainable agriculture (Chappell and LaValle 2011 ). Determination of the diversity and abundance of bird species is often contingent upon the complexity and composition of assemblages (Chao and Jost 2015 ; Ramírez and Gutiérrez-Fonseca 2016 ). The term assemblage is used in community ecology to refer to a group of taxonomically related species that occur together in space and time (Weiss and Ray 2019 ). Numerous studies have been conducted in Brazil (Pirela de Faría and Prieto de Alizo 2006 ), Guatemala (Greenberg et al. 1997 ), Venezuela (Jones et al. 2002 ), Mexico (Alvarez-Alvarez et al. 2022 ), and Colombia (Jones et al. 2023) to investigate the association between bird assemblages and coffee-production systems. The results of these studies show that bird species richness tends to be higher in forested environments, diverse agroforestry systems, and those production systems located near forest remnants in comparison to monocultures or agroforestry systems situated near fragmented forests. In Bolivia, increased bird richness has been linked to the structural complexity of vegetation in cocoa agroforestry systems (Naoki et al. 2017 ). Further, it has been shown that birds can contribute to ecosystem services in coffee-production systems, for example as controllers of borer disease ( Hypothenemus hampei ) (Landivar Albis 2018 ). Nevertheless, the composition of bird assemblages associated with specialty coffee-production systems in Bolivia has not yet been evaluated, despite the market growth of specialty coffee in Bolivia and beyond (Torrez et al. 2023 ; Jacobi et al. 2024 ). Notably, customers of such expensive high-quality coffee typically expect it to be environmentally sustainable (Giovannucci and Koekoek 2007). Against this background, evaluation of bird communities in different coffee-producing areas can shed light on the relationship between biodiversity and the production of specialty coffees. The present study aims to investigate the relationship between bird assemblages in what Perfecto et al. ( 2019 ) refer to as “Nature’s matrix”, highlighting that cropland is not isolated from its surrounding habitats. Indeed, the latter also belong to the wider agricultural landscape. The landscape configuration of the study region consists of coffee-production systems surrounded by secondary forests, thus providing suitable characteristics for evaluation of bird assemblages within the conceptual framework of Nature’s matrix. In the present study, we understand Nature’s matrix as a landscape that includes specialty coffee plots and secondary forests. Our working question is how bird assemblages vary between coffee plots and secondary forests in specialty coffee-production systems. We expect to find a lower diversity and richness of species in and around coffee plantations, as well as more generalist species and fewer specialists, due to the tendency of bird assemblages to homogenize in systems of lower plant complexity, such as coffee plantations, compared to secondary forests (Karp et al. 2012 ). Methods Study site The study was carried out in the municipality of Caranavi, in the department of La Paz, Bolivia. The average size of coffee-producing farms here is 2.6 hectares (ha), often shaded by trees Inga edulis (Torrico et al. 2024 ) and surrounded by secondary forest. Sampling was carried out in June 2021. We evaluated 49 sites selected following the criteria of Toledo and Moguel (2012) and Perfecto et al. ( 2019 ), considering coffee plots as part of Nature’s matrix, whereby agriculture is viewed as an integral component of the biodiversity conservation agenda (Perfecto et al. 2019 ). In our study, Nature’s matrix comprises coffee plots and secondary forests. We chose this approach because coffee plots in Caranavi, Bolivia, are surrounded by continuous forest or slightly fragmented secondary forest, in contrast to the intensive coffee-production landscapes of Brazil, Colombia, and Peru (Perfecto et al. 2019 ), where secondary forests are generally absent around coffee plantations. Initially, our sampling sites encompassed three types of systems: monocultures, agroforestry systems, and secondary forests. However, the monocultures were close to the forests. Each monoculture plot had a smaller distance to forests ( \(\:\stackrel{-}{x}\) : 108,3 a 194,5 m; \(\:\sigma\::\) 101,6 a 158,7 m) than the standard to guarantee the independence of sampling in birds, which is 250 m radius. Therefore, it is likely that their ird diversity is similar to agroforestry systems. For this reason, monocultures and agroforestry systems were grouped together as coffee plots. Our study site is located in Bolivia’s pre-montane humid subtropical forest region (Torrico-Albino et al. 2019). The average annual temperature here is 25.9 ºC, with mean annual precipitation of 1500–2600 mm and relative humidity varying between 70% and 80% (Stackhouse 2024 ). Humidity data were obtained from the Prediction of World Energy Resources (POWER) Project at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC), funded by the NASA Earth Sciences/Applied Sciences Program. Due to urbanization and expansion of the agricultural frontier, the local landscape is composed of different stages of secondary forests and crops (Navarro 2011 ). Coffee agroforestry systems are dominated by Inga spp ., as shade trees, in combination with several species of Citrus spp. , as well as other forest and fruit species such as Persea Americana and Musa paradisiaca L . (Torrico et al. 2024 ). We installed 29 circular plots (hereafter, plots) in the coffee-producing farms (13 were monocrops and 16 were agroforestry systems) and 15 plots (5 m in radius) in secondary forests of similar size following Deppe and Rotenberry (2008). These plots were installed within five agricultural communities located between 700 and 1500 m a.s.l (Villa El Carmen, Montaña Verde association located in the San Lorenzo canton; the community of Villa Caturapi, the community of Quijarro; Villa Copacabana and Bolinda). The plots are shown in Fig. 1 . Data collection The plots were established using the tallest tree in the designated area to configure the circular plot based on Celis-Murillo et al. ( 2012 ) and Suárez García ( 2017 ). Once the focal tree was selected, a 2 m radius was measured around it using a flexometer in the direction of the four cardinal points. In each plot, we estimated the percentage of vegetation cover (shade percentage), considering plant strata (growth type) such as trees, shrubs, young trees, herbaceous vegetation, and mosses. We measured the percentage of herbaceous leaf cover, mosses leaf cover, shrubs and tree leaf cover, as well as plant species richness. Tree species identification was carried out in the field for the most common species during another study (Torrico et al. 2024 ). We identified tree, herbaceous, shrub, and moss samples in different ways, including photography and expert consultation. Around 50% of samples were classified as morphotypes because we could not identify the species. Topographic variables were also measured, such as percentage and direction of slope (with a clinometer and a compass) as well as the presence of water bodies (presence/absence) within a 250 m radius of the sampling plots. To ensure accuracy and consistency in habitat description, all of these criteria were applied according to the bird habitat description protocols of Ralph et al. ( 1993 ); Celis-Murillo et al. ( 2012 ); Suárez García ( 2017 ). Bird richness and abundance were quantified using two methods: point count surveys (Ralph et al. 1993 ) and acoustic recordings (Celis-Murillo et al. 2012 ; Suárez García 2017 ), always carried out between 6:00 and 11:00 am (Greenberg et al. 1997 ) by the same observer (NA). Two types of microphones were used, a unidirectional microphone installed on a tripod in the direction of the largest source of song emission and a second (multidirectional) microphone, both positioned at the same height in all plots. There were no sound disturbances on the recordings, as one of the microphones was multidirectional and the unidirectional microphone was on a tripod. Both methods were applied at the same time by two persons. The first person was the first author and the second person a field assistant, who synchronized both unidirectional and multidirectional microphones immediately upon commencement of the point-count to enable acoustic recordings. Sampling at each plot lasted 10 minutes, to avoid pseudo-replication, as recommended by Celis-Murillo et al. ( 2012 ); Suárez García ( 2017 ). Bird assemblage composition We used rank-abundance curves to assess the dominance of species in both systems (secondary forests and coffee systems) and the relevant Nature’s matrix. The curves were created using R software version 4.2.2 (R Core Team 2024) and were used to visually represent the species composition of the different systems. Similarity in species composition between coffee plots and secondary forest plots was assessed using the proportional similarity (SP) index (Feinsinger 2004 ). The raw values of \(\:{p}_{\text{i}}\) for each species in both systems were examined, and the lower of the two values of \(\:{p}_{\text{i}}\) was reported \(\:{p}_{\text{i}\text{C}\text{o}\text{f}\text{f}\text{e}\text{e}}\:\text{o}\text{r}\:\:{p}_{\text{i}\text{F}\text{o}\text{r}\text{e}\text{s}\text{t}}\) . If a species was absent from one of the systems, the value given to this species was zero. Finally, all minimum values were summed, resulting in the species similarity index between the systems. $$\:SP=\:\sum\:_{}^{S}\text{min}({p}_{\text{i}\text{C}\text{o}\text{f}\text{f}\text{e}\text{e}}{p}_{\text{i}\text{F}\text{o}\text{r}\text{e}\text{s}\text{t}})$$ 1 Diversity, richness, and forest specialist and generalist species Species diversity was assessed using the Shannon-Weaver index with the Jost correction (Shannon and Weaver 1949 ; Jost and González-Oreja 2012 ; Chao and Jost 2015 ). This index considers both species richness and evenness. If one group of species is twice as abundant as another group, the first group will have twice the diversity value as the second group (Jost and González-Oreja 2012 ). Additionally, we employed Pielou’s evenness index to measure the relationship between observed diversity and expected maximum diversity with R studio’s vegan package (R Core Team 2022). The value of this index ranges between zero and one, where one indicates that all species are equally abundant. To evaluate the differences in species richness, total abundance of bird species, and abundance of generalist and specialists between the coffee plots and secondary forest plots, it was first necessary to rule out the possible system effect of these variables. System effects (for coffee and secondary forest plots) were evaluated using Generalized Linear Models (GLM). Residuals were adjusted to different distributions for each case (S6). For the analysis, the factor levels considered were coffee and secondary forest plots while the abundance of bird species in each plot was used as the response variable. Abundance was selected as the response variable due to correlations between species richness, abundance, and diversity. In all the cases, the model with the lowest Akaike Information Criteria corrected for small sample sizes (Delta AICc < 2) was selected as the best model (Anderson et al. 2008) and confirmed by means of a Likelihood Ratio test (LRT) comparing the best model against the null model. Analyses were carried out in R (version 4.2.2.; R Core Team 2022). We used the following packages: lme4 to build GLMs (Bates et al. 2014 ), MuMIn for model selection (Bartoń 2013 ), lmtest to verify model fit comparing best model vs. null model using the likelihood ratio test (LRT), and emeans for the post-hoc analysis, using pairwise Tukey tests (Okoye and Hosseini 2024 ). Due to a high level of overdispersion, the model was unable to be fitted. As a solution, we used system as a random effect to avoid overdispersion. Generalist and specialist species in the assemblage Our research analysed bird species in coffee plantations and secondary forests, focusing on their feeding groups to understand their habitat preferences according to generalist or forest specialist species. To do so, we followed the methodology of Aben et al. ( 2008 ), which requires information on the natural history of each species. To compile natural history information for each species, we consulted various free natural history sources, including Birds of the World (2024), eBird, Neotropical Ornithological Society (NOS), BirdLife, and Xeno-Canto. Following review of the natural history information, the species were classified as forest generalists or forest specialists according to their affinity with the habitat (Aben et al. 2008 ). Our study used generalized linear models and boxplots to evaluate whether there was a significant difference in the diversity, richness, or abundance of generalists and specialists between the two study systems. To graph this, we use box diagrams, where we include their comparison with the entire Nature's matrix. Assemblage and habitat conditions At each circular plot, we measured the elevation, waterbodies (presence/absence), direction of slope, percentage of slope, plant richness, plant strata (trees, shrubs, young trees, herbs, and mosses) of tree leaf cover, shrub leaf cover, herbaceous leaf cover, and mosses leaf cover, in line with the methodology of Ralph et al. ( 1993 ), Suárez-García et al. (2017), and Celis-Murillo et al. ( 2012 ). To evaluate the effects of habitat variables (independent variables) on species richness, as well as the abundance of generalists and specialists (response variables), we first explored the relation of each habitat variable and its interaction with each dependent variable. Then, we selected the independent variables and fitted different models. For the relationship of habitat variables, the numerical independent variables were scaled to standardize the data. As no differences were found for bird richness between systems, we put together the richness data from both systems (coffee and secondary forest plots). Next, we used a GLM to assess the relationship between bird richness and habitat variables, including a negative binomial distribution (Atkinson and Woods 2015 ). This was done to handle overdispersed count data. We considered system level as a random factor, because habitat variables were taken from different system levels (coffee plots and secondary forest) (Bolker 2015 ). Results Bird assemblage composition A total of 1,010 individuals of 124 bird species were recorded, eight of which were migratory while the rest were resident birds. We found 68 bird species in secondary forest plots and 94 species in the coffee plots (Fig. 2 . a-c); 38 species were shared between coffee and secondary forest plots, of which 21 were specialists and 17 were generalists; 56 species were exclusive to the coffee plots, of which 24 were generalists and 32 were specialists; 30 species were exclusive to secondary forests, of which 15 were specialists and 15 were generalists. The rank-abundance curves (Fig. 2 . d) point to greater species richness in the coffee plots compared to the forest plots. Regarding the rank-abundance curves, each bird species is represented by a line on the graph that indicates its rank in terms of abundance and its relative abundance within the habitat. Judging by the differing width of the rank-abundance curves, species dominance between the coffee and secondary forest plots is not similar (Fig. 2 . d). The curves also indicate that the five most-common species differ in their distribution across the coffee and secondary forest plots and the entirety of Nature’s matrix. The extension of the curve line for secondary forest plots suggests slightly more species stability compared to coffee plots, but greater stability is observed throughout Nature’s matrix. Steep slopes on the rank-abundance curves indicate unequal distribution of species abundance. A pronounced numerical dominance of Psarocolius decumanus (a generalist species) is indicated for coffee plots while a dominance of Myiodynastes maculatus (a specialist species) is seen for secondary forest plots (Fig. 2 . d). For Nature’s matrix (sum of both systems), it can be seen how each system contributes different dominant species to the full assemblage. Psarocolius decumanus (a generalist species) displays the highest relative abundance and appears dominant in coffee plots; followed by Myiodynastes maculatus (a specialist species) that appears dominant in secondary forest plots (Fig. 2 . d). The dominant generalist and specialist species can be seen in S1–S3. Of the five most dominant species in coffee and secondary forest plots and throughout Nature’s matrix, the dominant frugivorous and omnivorous species observed were Psarocolius decumanus and Pionus menstruus and Ramphastos vitellinus , followed by a granivorous bird, Patagioenas plumbea , and an insectivore, Myiodynastes maculatus. The results of analysis of the similarity index point to low species similarity between coffee and secondary forest plots. We found only 0.35 (35%) similarity of all species between the coffee and secondary forest plots, as well as 0.3 (30%) similarity of generalist species and 0.26 (26%) similarity of specialist species between both systems. These results suggest that the assemblages of coffee and secondary forest plots do not feature similar species composition. Diversity, richness of assemblages According to our analysis, coffee plots and secondary forest plots did not differ in terms of overall bird diversity (LRT: χ² = 0.048, d.f. = 1, P = 0.825, ΔAIC = 2.26; Fig. 3 a-d, S4), abundance (LRT: χ² = 0.7497, d.f. = 1, P = 0.386, ΔAIC = 1.56; Fig. 3 . a-d, S4), Pielou evenness (LRT: χ² = 0.17, d.f. = 1, P = 0.679, ΔAIC = 2.14; Fig. 3 . a-d, S4), and richness (LRT: χ² = 0.002, d.f. = 1, P = 0.965, ΔAIC = 2.31; Fig. 3 . a-d, S4). However, when we distinguish between generalist and specialist species, our analysis shows that secondary forest plots bore higher specialist species richness than coffee plots, whereas the assemblage of generalist species was richer in coffee plots (Fig. 3 .) Secondary forest plots featured lower variation in species richness and abundance for generalist species compared to coffee plots. However, secondary forest plots displayed significantly higher values for specialist species compared to coffee plots (Fig. 3 and S5 and Table 1 ). Table 1 Best models’ parameter estimates for the variables that showed variation explained by the system level (see S5) for generalist and specialist species. Group Variable Parameter Estimate SD z value p – value Generalists Richness (Intercept: Coffee plot) 1.574 0.101 15.648 < 0.001 Forest -0.432 0.193 -2.422 < 0.05 Abundance (Intercept: Coffee plot) 1.9557 0.112 17.491 < 0.001 Forest -0.621 0.212 -2.933 < 0.01 Specialists Richness (Intercept: Coffee plot) 1.351 0.114 11.823 < 0.001 Forest 0.372 0.182 2.047 < 0.05 Diversity (Intercept: Coffee plot) 1.116 0.107 10.44 ( t -value) < 0.001 Forest 0.448 0.183 2.45 ( t- value) < 0.05 Assemblages and habitat conditions Only herbaceous leaf cover displayed a significant negative effect on all bird species richness, representing the entire assemblage of birds in Nature’s matrix (LRT: χ² = 4.754, d.f. = 1, P < 0.05, ΔAIC = 2.45; Fig. 4 ., S7, and Table 2 ). We chose this variable because all the assemblage variables (richness, diversity, abundance) were correlated. We found that herbaceous leaf cover, leaf cover of shrubs, and plant strata explained the variation of abundance of generalists across Nature’s matrix (LRT: χ² = 66.05, d.f. = 1, P < 0.001, ΔAIC = 58.76; Fig. 4 ., S7 and Table 2 ). Shrub leaf cover displayed significant positive effects on the abundance of generalists. The number of herbaceous leaf cover and plant strata was negatively related to the abundance of generalists (Fig. 4 ., S7, and Table 2 ). None of the environmental variables explained variation in the abundance of specialists (LRT: χ² = 1.901, d.f. = 1, P = 167, ΔAIC = 0.52, S7 and Table 2 ). Table 2 GLMM estimates averaged from the best models of abundance of generalists with delta AICc lower than two (see S8) Group Variable Estimate Std. Error z value Pr(>|z|) Bird species richness (Intercept) 2.111 0.125 16.940 < 0.001 Herbaceous leaf cover -0.205 0.100 2.044 < 0.05 Waterbody 0.069 0.151 0.463 0.643 Plant richness 0.013 0.049 0.257 0.797 Plant strata 0.012 0.052 0.225 0.822 Abundance of generalists (Intercept) 2.370 0.050 47.715 < 0.001 Plant strata -0.363 0.081 4.468 < 0.001 Herbaceous leaf cover 0.165 0.078 2.114 < 0.05 Shrub leaf cover 0.270 0.067 4.033 < 0.001 Elevation 0.018 0.041 0.454 0.650 Abundance of specialists (Intercept) 1.634 0.150 10.879 < 0.001 Elevation 0.047 0.087 0.537 0.591 Waterbody 0.025 0.101 0.252 0.801 Herbaceous leaf cover 0.009 0.045 0.208 0.835 Shrub leaf cover 0.022 0.065 0.331 0.74 Discussion Our research found that small-scale, forest-adjacent coffee plantations managed by traditional producers can host a diversity of bird assemblages comparable to or even greater than that of surrounding natural forests, refuting the hypothesis that coffee plantations always have lower diversity and richness of birds (Karp et al. 2012 ). Indeed, our analysis found even more bird species in coffee plantations than in secondary forests (Faminow and Ariza 2001 ; Jones et al. 2002 ; Leyequien et al. 2006 ; Ocampo-Ariza et al. 2024 ). Overall, landscape characteristics such as the amount of forest cover and vegetation heterogeneity positively influence the diversity and composition of bird communities (Alexandrino et al. 2017 ; Perfecto et al. 2019 ; Marconi and Armengot 2020 ). Specifically, we identified greater numbers of generalist and specialist species in the assemblages of coffee plantations compared to those of forests alone. This likely results from the special configuration of Nature’s matrix, featuring diverse habitats benefitting both bird groups, to which the small-scale coffee plantations contribute (Alexandrino et al. 2017 ; Ocampo-Ariza et al. 2024 ; Martínez-Penados et al. 2024 ). Variation of bird assemblages between coffee plots and secondary forests in specialty coffee production systems Our research refutes the hypothesis that coffee-plot bird assemblages necessarily feature less diversity and richness than secondary forest plots (Karp et al. 2012 ). No significant difference was found in the diversity, richness, or abundance of bird species between the two systems. Indeed, more bird species were quantified in the coffee-plot assemblages compared to those of the secondary forest plots. According to Faminow and Ariza ( 2001 ); Jones et al. ( 2002 ); Leyequien et al. ( 2006 ); Perfecto et al. ( 2019 ); Ocampo-Ariza et al. ( 2024 ), coffee systems such as those we researched – i.e. configured on a small-scale by traditional producers and surrounded by forest – display bird diversity comparable to or greater than natural forests. In addition, according to Perfecto et al. ( 2019 ), landscapes like those in the present study feature greater biodiversity than landscapes dominated by large coffee plantations. We identified 94 bird species in our coffee plots and 68 in secondary forests. In a separate study, Ong’ondo et al. ( 2022 ) recorded 127 bird species in shade coffee plantations and 79 in forests. These findings support the hypothesis that coffee small-scale plantations can host more bird species than forests alone, even if they are monocultures or agroforestry systems like those in our study, in particular when they are configured near to forests. Further, our analysis revealed a similarity index of 35% when comparing the species composition of coffee-plot and forest-plot bird assemblages, not unlike the findings (similarity index 42%) of Ong’ondo et al. ( 2022 ). This low similarity value indicates that coffee plantations cannot substitute for natural forests, but can rather serve as complementary sites on behalf bird conservation. In particular, our results confirm this in terms of dominant and exclusive species found in each system. Specifically, we found that Myiodynastes maculatus and Ramphastos vitellinus (specialist birds) were dominant in secondary forest plots, whereas Psarocolius decumanus and Pionus menstruus (generalist birds) were dominant in coffee plots. We found 30 species exclusive to secondary forest plots and 56 exclusive to coffee plots. These findings are also in line with those of Ong'ondo et al. (2022), who identified 18 bird species exclusive to forests and 66 exclusive to coffee systems. According to de Souza Leite et al. ( 2022 ), landscape characteristics explain variations in the abundance and presence/absence of birds identified in such environments. According to Harvey and González Villalobos ( 2007 ), differences in habitat and vegetation generate filters that favour certain birds, giving rise to dissimilar communities between natural habitats and agroecosystems such as those observed in our study. The Nature’s matrix created by the complementary plots in our study enables maintenance of exclusive assemblages of species according to morphological attributes (Ong’ondo et al. 2022 ). In terms of generalist and specialist species, our results refute the hypothesis that forest assemblages feature more specialist species while coffee plantations feature more generalist species. Indeed, we found higher numbers of both groups in coffee plots. According to Alexandrino et al. ( 2017 ); Ocampo-Ariza et al. ( 2024 ), landscape-scale forest cover positively influences bird communities, especially forest and fruit-eating bird species like those in our study. The small-scale coffee plantations we studied were surrounded by forest cover, regardless of whether they were monocultures or agroforestry systems. According to Velásquez-Trujillo et al. ( 2021 ), a configuration like gives rise to greater species diversity than secondary forests. In terms of generalist species, we found significant differences in richness and abundance between the systems analysed. Specifically, we identified greater richness and abundance of generalists in the coffee plots compared to the secondary forest plots. However, no significant difference was found in terms of the diversity of generalists. These findings may be attributed to the landscape configuration. According to Muhamad et al. ( 2013 ) and Alexandrino et al. ( 2017 ), nearby forests and scattered trees in agricultural landscapes can act as corridors and “islands” that facilitate the movement and presence of bird species, including generalists and insectivores. In the agroforestry systems we studied, Torrico et al. ( 2024 ) found more than 85 different plant species as shade trees. This suggests that the landscape configuration of our systems could explain the differences observed with regard to generalist bird species. In addition, we found significant differences regarding the richness and diversity of specialist bird species between coffee plots and secondary forest plots. In particular, we found greater richness and diversity of specialist bird species in the secondary forest plots. However, contrary to our hypothesis, we observed a higher number of pecialists in coffee plots compared to secondary forest plots. These patterns of diversity and richness of specialists could relate to the amount of forest cover present at the landscape scale, which was the same in the two systems under study (Alexandrino et al. 2017 ). In our Nature’s matrix and that found elsewhere, forest cover positively influences the diversity and composition of bird communities (Robertson et al. 2013 ). Further, the heterogeneity of the landscape in contexts such as ours provides good conditions for specialist species, in line with Alexandrino et al. ( 2017 ) and Tavares et al. ( 2019 ). According to our results, the configuration of the wider landscape – particularly the amount of forest covers present and the heterogeneity of the vegetation structure belonging to Nature’s matrix – may modulate the effects of plot-level homogenization on the diversity and composition of bird communities. This is consistent with patterns identified by Alexandrino et al. ( 2017 ), Velásquez-Trujillo et al. ( 2021 ), Ocampo-Ariza et al. ( 2024 ) with regard to agricultural landscapes. According to Ocampo-Ariza et al. ( 2024 ), it is necessary to consider these dynamics at multiple scales in order to develop effective conservation strategies in the context of specialty coffee production systems. Effect of habitat on assemblages The richness of all bird species was significantly affected by the percentage of herbaceous leaf cover: the greater the herbaceous leaf cover, the lower the bird species richness. No significant effects were found with respect to percentages of shrub leaf cover, plant richness, plant strata, elevation, or waterbodies. This could be because the bird species in our study were common to a particular stratum, similar to the species in the study by Jones et al. ( 2002 ). Our results support this because plant strata and the percentage of herbaceous leaf cover have a negative effect on the abundance of generalists. By contrast, the percentage of shrub leaf cover has a positive effect on the abundance of generalists. Buechley et al. ( 2015 ) indicate that different types of shade management in coffee plantations can generate more diverse vegetation cover, including trees, grasses, and shrubs – that benefit bird species. Overall, according to Jones et al. ( 2002 ), shrubs are more common in forests and understory birds use coffee plantations as a conduit to travel between shrubs in search of food. Therefore, the bird species in our study, especially the generalists, are likely understory birds for whom shrub cover is particularly relevant. In line with Bhagwat et al. (2008), this indicates that the amount of forest cover present at the landscape scale is key to the diversity and composition of bird communities. Shrub cover in forests and coffee plantations benefits understory birds, as observed by Jones et al. ( 2002 ) and Buechley et al. ( 2015 ). These bird species can use it as runners in search of food (Jones et al. 2002 ; Alexandrino et al. 2017 ). The variables of elevation, plant richness, and waterbodies had no significant effect on the abundance of generalists. While these elements belong to Nature’s matrix Perfecto et al. ( 2019 ), they do not appear to make a direct contribution to bird diversity in the context of specialty coffee landscapes. According to de Souza Leite et al. ( 2022 ), landscape-scale factors are more important than plot- or patch-level factors in determining bird diversity patterns. No habitat variables affected specialist abundance in the present study. This indicates that our Nature’s matrix was high quality and featured less contrast between habitats, precluding edge effects like predation and parasitism that particularly impact specialists (Leyequien et al. 2006 ). According to de Souza Leite et al. ( 2022 ), natural corridors that enable birds to access forests are particularly important, as habitat specialist species depend on forest environments for their reproduction and survival. Overall, our findings indicate that the landscape configuration of Nature’s matrix – including forest cover, habitat heterogeneity, and connecting elements – plays a key role in modulating habitat effects on biodiversity (Olson and Dinerstein 1998 ; Olden and Rooney 2006 ; García and Martínez 2012 ). In this way, maintaining a variety of habitats within Nature’s matrix – including coffee plantations featuring diverse cover (Buechley et al. 2015 ) and surrounded by forests (de Souza Leite et al. 2022 ) – can benefit both generalist and specialist bird species (Martínez-Penados et al. 2024 ) see Fig. 5 . Conclusion In this research, no variation was found in bird communities between coffee plots and secondary forests in specialty coffee production systems.. Our study found that small-scale coffee plantations (monocultures and agroforestry systems) can host levels of bird diversity comparable to or even greater than that of natural forests, refuting the hypothesis that coffee plantations necessarily have lower bird diversity and richness. This appears related to special characteristics of the landscape. Contrary to our predictions, a higher number of generalist and specialist species were found in coffee plantations compared to forests. This may be attributed to the configuration of Nature’s matrix, in particular when it encompasses diverse habitats beneficial to both specialist and generalist bird species. We conclude that specialty coffee plots can provide habitats that complement those of neighbouring forests in conserving bird species. As evidenced by our study, dispersed coffee plots surrounded by forest cover can host species compositions similar to those found in secondary forests. Declarations Funding This research received funding from the project “Exploring the interlinkages between specialty coffee farms biodiversity and farmer’s practices in Bolivia” Seed Money Grant 1912, with the financial support of the Leading House for the Latin American Region, Latin American Swiss Center (CLS-HSG), University of St. Gallen, and the State Secretariat for Education, Research and Innovation (SERI), Switzerland. Author Contribution All authors contributed to the main text of the manuscript, but NA had the greatest involvement, NA prepared Figures 1 to 5. All authors reviewed the manuscript. GT contributed with forums for the study area. VT contributed to the map of the study area. Acknowledgement We thank the coffee farmers of Caranavi for allowing us access to their farms. Special thanks also go to D. Mendez for lending us the recording equipment, to D. Camacho for helping us with some bird identifications, and to our field guides: S.R. López-Gutiérrez, S. Pastén, and S.V. Condori-Coarite. The research was funded by a seed grant from the Swiss Secretariat for Education, Research, and Innovation (SERI). Data Availability Los datos de diversidad riqueza y abundancia respaldan los hallazgos de este estudio junto con los datos de abundancia de especies generalistas y especialistas. Los datos se proporcionan dentro del manuscrito y en los archivos de información complementaria. References Aben J, Dorenbosch M, Herzog SK, et al (2008) Human Disturbance affects a Deciduous Forest Bird Community in the Andean Foothills of Central Bolivia. Bird Conserv Int 18:363–380. https://doi.org/10.1017/S0959270908007326 Alexandrino ER, Buechley ER, Karr JR, et al (2017) Bird based Index of Biotic Integrity: Assessing the ecological condition of Atlantic Forest patches in human-modified landscape. 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Springer Nature, Singapore, pp 187–209 Olden JD, Rooney TP (2006) On defining and quantifying biotic homogenization. Glob Ecol Biogeogr 15:113–120. https://doi.org/10.1111/j.1466-822X.2006.00214.x Olson DM, Dinerstein E (1998) The Global 200: A Representation Approach to Conserving the Earth’s Most Biologically Valuable Ecoregions. Conserv Biol 12:502–515 Ong’ondo FJ, Fogarty FA, Njoroge P, Johnson MD (2022) Bird abundance and diversity in shade coffee and natural forest in Kenya. Glob Ecol Conserv 39:e02296. https://doi.org/10.1016/j.gecco.2022.e02296 Perfecto I, Vandermeer J, Wright A (2019) Nature’s Matrix: Linking Agriculture, Biodiversity Conservation and Food Sovereignty, 2nd edn. Routledge, London Pirela de Faría L, Prieto de Alizo L (2006) Perfil de competencias del docente en la función de investigador y su relación con la producción intelectual. Opción 22:159–177 Ralph CJ, Geupel GR, Pyle P, et al (1993) Handbook of field methods for monitoring landbirds. Albany CA US Dep Agric For Serv Pac Southwest Res Stn Gen Tech Rep PSW-GTR-144 41 P 144:. https://doi.org/10.2737/PSW-GTR-144 Ramírez A, Gutiérrez-Fonseca PE (2016) Sobre ensambles y ensamblajes ecológicos - respuesta a Monge-Nájera. Rev Biol Trop 64:817–819. https://doi.org/10.15517/rbt.v64i2.21232 Robertson H, Dowding J, Elliott G, et al (2013) Conservation status of New Zealand birds, 2012 Shannon CE, Weaver W (1949) The mathematical theory of communication. University of Illinois Press, Champaign, IL, US Stackhouse P (2024) NASA POWER | DAVe v2.3.4 Suárez García CJ (2017) Estigma, communitas y modos de corrección para los habitantes de la calle en Bogotá (2000-2010). Soc Econ 195–216 Tavares PD, Uzêda MC, Pires A dos S (2019) Biodiversity Conservation in Agricultural Landscapes: the Importance of the Matrix. Floresta E Ambiente 26:e20170664. https://doi.org/10.1590/2179-8087.066417 Torrez V, Benavides-Frias C, Jacobi J, Speranza CI (2023) Ecological quality as a coffee quality enhancer. A review. Agron Sustain Dev 43:19. https://doi.org/10.1007/s13593-023-00874-z Torrico GG, Antezana Alvarado N, Pacheco LF, et al (2024) Socioeconomic and biophysical factors affect tree diversity in farms producing specialty coffee in Caranavi, Bolivia. Agrofor Syst 98:427–439. https://doi.org/10.1007/s10457-023-00920-5 Velásquez-Trujillo V, Betancurt-Grisales JF, Vargas-Daza AM, et al (2021) Bird Functional Diversity in Agroecosystems and Secondary Forests of the Tropical Andes. Diversity 13:493. https://doi.org/10.3390/d13100493 Weiss KCB, Ray CA (2019) Unifying functional trait approaches to understand the assemblage of ecological communities: synthesizing taxonomic divides. Ecography 42:2012–2020. https://doi.org/10.1111/ecog.04387 Birds of the World - Comprehensive life histories for all bird species and families. https://birdsoftheworld.org/bow/home. Accessed 18 Jul 2024 Explorar - eBird. https://ebird.org/explore. Accessed 18 Jul 2024a Neotropical Ornithological Society - Neotropical Ornithological Society. https://ornitologianeotropical.org/. Accessed 18 Jul 2024b BirdLife International - BirdLife is the world leader in Bird Conservation. https://www.birdlife.org/. Accessed 18 Jul 2024c xeno-canto :: Sharing wildlife sounds from around the world. https://xeno-canto.org/. Accessed 18 Jul 2024d Matrix quality determines the strength of habitat loss filtering on bird communities at the landscape scale - Souza Leite - 2022 - Journal of Applied Ecology - Wiley Online Library. https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/1365-2664.14275. Accessed 18 Jul 2024e Additional Declarations No competing interests reported. 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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-4825928","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":345185403,"identity":"f19c3412-708a-4948-8902-285bfeb46282","order_by":0,"name":"Nayra Antezana-Alvarado","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYNCDD0DMxk6cWgMwyTgDpIWZFC3MPGCSgFpz9jNmH37u+CMn33724GebX9vk+ZgZGD98zMGtxbInx3hm7xkDY8aevGTp3L7bhm3MDMySM7fhcdGBHGMG3jaDxGaGHAPp3J7bjEAtbMy8+LScf2PM+LfNoL6N/43xb8ue2/aEtdzIMWYG2pLAI5FjJs3w43YiEVqeFTPLthkbzpB4Y2bZ23A7uY2ZsRm/X84nb2Z82yYnL9+fY3zjx5/btvPbmw9++IhHCwMDhwGCzdgGJhvwqQcC9gdInD8EFI+CUTAKRsGIBAAURUue2Ev18wAAAABJRU5ErkJggg==","orcid":"","institution":"Universidad Mayor de San Andrés","correspondingAuthor":true,"prefix":"","firstName":"Nayra","middleName":"","lastName":"Antezana-Alvarado","suffix":""},{"id":345185404,"identity":"3fe7b72d-04e8-4fb8-bec6-74a2713a930a","order_by":1,"name":"Gabriel G. Torrico","email":"","orcid":"","institution":"Universidad Mayor de San Andrés","correspondingAuthor":false,"prefix":"","firstName":"Gabriel","middleName":"G.","lastName":"Torrico","suffix":""},{"id":345185405,"identity":"a2702ffb-96a8-4326-b131-861195f7cd03","order_by":2,"name":"Luis F. 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Image source: Vincent Voss (2024)\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4825928/v1/1982dac456a4f332b5c0f559.png"},{"id":63443486,"identity":"2c732853-be94-4c44-9a4d-8d8c66445e0a","added_by":"auto","created_at":"2024-08-28 08:04:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":103404,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphic representation of the variables for all bird species a. \u003c/strong\u003erichness of all bird species; \u003cstrong\u003eb. \u003c/strong\u003eabundance of all bird species; \u003cstrong\u003ec. \u003c/strong\u003eShannon Index for all bird species; \u003cstrong\u003ed.\u003c/strong\u003e Pielou Index for all bird species;\u003cstrong\u003e e. \u003c/strong\u003erichness of generalists; \u003cstrong\u003ef. \u003c/strong\u003eabundance of generalists; \u003cstrong\u003eg. \u003c/strong\u003erichness of specialists; and \u003cstrong\u003eh. \u003c/strong\u003eShannon Index diversity of specialists; significant differences are highlighted (*\u0026lt;0.05, **\u0026lt;0.01).\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4825928/v1/ad5a406c5a771b8b51c123aa.png"},{"id":63444230,"identity":"6f01949f-ac19-4acf-883e-c56854e604b4","added_by":"auto","created_at":"2024-08-28 08:12:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":73703,"visible":true,"origin":"","legend":"\u003cp\u003eBird richness and abundance of generalist species in relation to environmental variables. Points represent the observed values. The blue line represents the adjusted linear function of the data, surrounded by a grey space representing the fitted errors.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4825928/v1/152ad458b735cf5b14b50817.png"},{"id":63443490,"identity":"a13fa2c4-c179-431f-8c80-5d920051c586","added_by":"auto","created_at":"2024-08-28 08:04:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":266565,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eContribution to Nature’s matrix of bird assemblages from specialty coffee plots and secondary forest plants. \u003c/strong\u003eIllustration of the abundance and richness of generalist species, richness and abundance of specialist species, as well as significant habitat variables like plant strata, herbaceous leaf cover, and shrub leaf cover (author elaboration using BioRender.com).\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4825928/v1/29d42f89f94d1a1cbbfb7e67.png"},{"id":83907645,"identity":"1c650b13-7ba9-480d-9a2c-7ecc92a96996","added_by":"auto","created_at":"2025-06-04 10:47:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1841787,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4825928/v1/c8804e02-7672-4d84-b228-25127835d6a2.pdf"},{"id":63444232,"identity":"ddc35f60-b731-4c0c-b0c3-d5d135feb205","added_by":"auto","created_at":"2024-08-28 08:12:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":257553,"visible":true,"origin":"","legend":"","description":"","filename":"SIInfocomplementariaNayraAntezanaJul.30.24.docx","url":"https://assets-eu.researchsquare.com/files/rs-4825928/v1/ce3c4088a4effa9846176b94.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bird assemblages in specialty coffee production landscapes in pre-montane humid subtropical forests","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLand use practices can have varying impacts on local plant and animal species (Gil-Tena et al. 2009; Newbold et al. 2013; Prokopov\u0026aacute; et al. 2019; Sch\u0026uuml;rings et al. 2022). One possible impact is biotic homogenization, meaning a process of increasing species invasions and extinctions resulting in greater taxonomic, genetic, or functional similarity between two or more biotas over time (Olden and Rooney \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Agroforestry and its associated management practices have been identified as potential means of promoting biodiversity (Marconi and Armengot \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Of special relevance here is the theoretical framework proposed by Perfecto et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), in particular the concept of \u0026ldquo;Nature\u0026rsquo;s matrix\u0026rdquo; emphasizing that agriculture is part of a broader ecological matrix. This means that natural habitats, such as forests and rivers, interact with and impact agricultural areas. In this way, studying production systems using a Nature\u0026rsquo;s matrix-approach can improve our understanding of the contribution of biodiversity to sustainable agriculture (Chappell and LaValle \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDetermination of the diversity and abundance of bird species is often contingent upon the complexity and composition of assemblages (Chao and Jost \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ram\u0026iacute;rez and Guti\u0026eacute;rrez-Fonseca \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The term assemblage is used in community ecology to refer to a group of taxonomically related species that occur together in space and time (Weiss and Ray \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Numerous studies have been conducted in Brazil (Pirela de Far\u0026iacute;a and Prieto de Alizo \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), Guatemala (Greenberg et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), Venezuela (Jones et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), Mexico (Alvarez-Alvarez et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and Colombia (Jones et al. 2023) to investigate the association between bird assemblages and coffee-production systems. The results of these studies show that bird species richness tends to be higher in forested environments, diverse agroforestry systems, and those production systems located near forest remnants in comparison to monocultures or agroforestry systems situated near fragmented forests. In Bolivia, increased bird richness has been linked to the structural complexity of vegetation in cocoa agroforestry systems (Naoki et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Further, it has been shown that birds can contribute to ecosystem services in coffee-production systems, for example as controllers of borer disease (\u003cem\u003eHypothenemus hampei\u003c/em\u003e) (Landivar Albis \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Nevertheless, the composition of bird assemblages associated with specialty coffee-production systems in Bolivia has not yet been evaluated, despite the market growth of specialty coffee in Bolivia and beyond (Torrez et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jacobi et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Notably, customers of such expensive high-quality coffee typically expect it to be environmentally sustainable (Giovannucci and Koekoek 2007). Against this background, evaluation of bird communities in different coffee-producing areas can shed light on the relationship between biodiversity and the production of specialty coffees.\u003c/p\u003e \u003cp\u003eThe present study aims to investigate the relationship between bird assemblages in what Perfecto et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) refer to as \u0026ldquo;Nature\u0026rsquo;s matrix\u0026rdquo;, highlighting that cropland is not isolated from its surrounding habitats. Indeed, the latter also belong to the wider agricultural landscape. The landscape configuration of the study region consists of coffee-production systems surrounded by secondary forests, thus providing suitable characteristics for evaluation of bird assemblages within the conceptual framework of Nature\u0026rsquo;s matrix. In the present study, we understand Nature\u0026rsquo;s matrix as a landscape that includes specialty coffee plots and secondary forests. Our working question is how bird assemblages vary between coffee plots and secondary forests in specialty coffee-production systems. We expect to find a lower diversity and richness of species in and around coffee plantations, as well as more generalist species and fewer specialists, due to the tendency of bird assemblages to homogenize in systems of lower plant complexity, such as coffee plantations, compared to secondary forests (Karp et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy site\u003c/h2\u003e \u003cp\u003eThe study was carried out in the municipality of Caranavi, in the department of La Paz, Bolivia. The average size of coffee-producing farms here is 2.6 hectares (ha), often shaded by trees \u003cem\u003eInga edulis\u003c/em\u003e (Torrico et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and surrounded by secondary forest. Sampling was carried out in June 2021. We evaluated 49 sites selected following the criteria of Toledo and Moguel (2012) and Perfecto et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), considering coffee plots as part of Nature\u0026rsquo;s matrix, whereby agriculture is viewed as an integral component of the biodiversity conservation agenda (Perfecto et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In our study, Nature\u0026rsquo;s matrix comprises coffee plots and secondary forests. We chose this approach because coffee plots in Caranavi, Bolivia, are surrounded by continuous forest or slightly fragmented secondary forest, in contrast to the intensive coffee-production landscapes of Brazil, Colombia, and Peru (Perfecto et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), where secondary forests are generally absent around coffee plantations. Initially, our sampling sites encompassed three types of systems: monocultures, agroforestry systems, and secondary forests. However, the monocultures were close to the forests. Each monoculture plot had a smaller distance to forests (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{x}\\)\u003c/span\u003e\u003c/span\u003e: 108,3 a 194,5 m; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sigma\\::\\)\u003c/span\u003e\u003c/span\u003e 101,6 a 158,7 m) than the standard to guarantee the independence of sampling in birds, which is 250 m radius. Therefore, it is likely that their ird diversity is similar to agroforestry systems. For this reason, monocultures and agroforestry systems were grouped together as coffee plots.\u003c/p\u003e \u003cp\u003eOur study site is located in Bolivia\u0026rsquo;s pre-montane humid subtropical forest region (Torrico-Albino et al. 2019). The average annual temperature here is 25.9 \u0026ordm;C, with mean annual precipitation of 1500\u0026ndash;2600 mm and relative humidity varying between 70% and 80% (Stackhouse \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Humidity data were obtained from the Prediction of World Energy Resources (POWER) Project at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC), funded by the NASA Earth Sciences/Applied Sciences Program. Due to urbanization and expansion of the agricultural frontier, the local landscape is composed of different stages of secondary forests and crops (Navarro \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Coffee agroforestry systems are dominated by \u003cem\u003eInga spp\u003c/em\u003e., as shade trees, in combination with several species of \u003cem\u003eCitrus spp.\u003c/em\u003e, as well as other forest and fruit species such as \u003cem\u003ePersea Americana\u003c/em\u003e and \u003cem\u003eMusa paradisiaca L\u003c/em\u003e. (Torrico et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe installed 29 circular plots (hereafter, plots) in the coffee-producing farms (13 were monocrops and 16 were agroforestry systems) and 15 plots (5 m in radius) in secondary forests of similar size following Deppe and Rotenberry (2008). These plots were installed within five agricultural communities located between 700 and 1500 m a.s.l (Villa El Carmen, Monta\u0026ntilde;a Verde association located in the San Lorenzo canton; the community of Villa Caturapi, the community of Quijarro; Villa Copacabana and Bolinda). The plots are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThe plots were established using the tallest tree in the designated area to configure the circular plot based on Celis-Murillo et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Su\u0026aacute;rez Garc\u0026iacute;a (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Once the focal tree was selected, a 2 m radius was measured around it using a flexometer in the direction of the four cardinal points.\u003c/p\u003e \u003cp\u003eIn each plot, we estimated the percentage of vegetation cover (shade percentage), considering plant strata (growth type) such as trees, shrubs, young trees, herbaceous vegetation, and mosses. We measured the percentage of herbaceous leaf cover, mosses leaf cover, shrubs and tree leaf cover, as well as plant species richness. Tree species identification was carried out in the field for the most common species during another study (Torrico et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). We identified tree, herbaceous, shrub, and moss samples in different ways, including photography and expert consultation. Around 50% of samples were classified as morphotypes because we could not identify the species. Topographic variables were also measured, such as percentage and direction of slope (with a clinometer and a compass) as well as the presence of water bodies (presence/absence) within a 250 m radius of the sampling plots. To ensure accuracy and consistency in habitat description, all of these criteria were applied according to the bird habitat description protocols of Ralph et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1993\u003c/span\u003e); Celis-Murillo et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e); Su\u0026aacute;rez Garc\u0026iacute;a (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBird richness and abundance were quantified using two methods: point count surveys (Ralph et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) and acoustic recordings (Celis-Murillo et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Su\u0026aacute;rez Garc\u0026iacute;a \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), always carried out between 6:00 and 11:00 am (Greenberg et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) by the same observer (NA). Two types of microphones were used, a unidirectional microphone installed on a tripod in the direction of the largest source of song emission and a second (multidirectional) microphone, both positioned at the same height in all plots. There were no sound disturbances on the recordings, as one of the microphones was multidirectional and the unidirectional microphone was on a tripod. Both methods were applied at the same time by two persons. The first person was the first author and the second person a field assistant, who synchronized both unidirectional and multidirectional microphones immediately upon commencement of the point-count to enable acoustic recordings. Sampling at each plot lasted 10 minutes, to avoid pseudo-replication, as recommended by Celis-Murillo et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e); Su\u0026aacute;rez Garc\u0026iacute;a (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBird assemblage composition\u003c/h2\u003e \u003cp\u003eWe used rank-abundance curves to assess the dominance of species in both systems (secondary forests and coffee systems) and the relevant Nature\u0026rsquo;s matrix. The curves were created using R software version 4.2.2 (R Core Team 2024) and were used to visually represent the species composition of the different systems. Similarity in species composition between coffee plots and secondary forest plots was assessed using the proportional similarity (SP) index (Feinsinger \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The raw values of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e for each species in both systems were examined, and the lower of the two values of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e was reported \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{\\text{i}\\text{C}\\text{o}\\text{f}\\text{f}\\text{e}\\text{e}}\\:\\text{o}\\text{r}\\:\\:{p}_{\\text{i}\\text{F}\\text{o}\\text{r}\\text{e}\\text{s}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e. If a species was absent from one of the systems, the value given to this species was zero. Finally, all minimum values were summed, resulting in the species similarity index between the systems.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:SP=\\:\\sum\\:_{}^{S}\\text{min}({p}_{\\text{i}\\text{C}\\text{o}\\text{f}\\text{f}\\text{e}\\text{e}}{p}_{\\text{i}\\text{F}\\text{o}\\text{r}\\text{e}\\text{s}\\text{t}})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eDiversity, richness, and forest specialist and generalist species\u003c/h2\u003e \u003cp\u003eSpecies diversity was assessed using the Shannon-Weaver index with the Jost correction (Shannon and Weaver \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1949\u003c/span\u003e; Jost and Gonz\u0026aacute;lez-Oreja \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Chao and Jost \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This index considers both species richness and evenness. If one group of species is twice as abundant as another group, the first group will have twice the diversity value as the second group (Jost and Gonz\u0026aacute;lez-Oreja \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Additionally, we employed Pielou\u0026rsquo;s evenness index to measure the relationship between observed diversity and expected maximum diversity with R studio\u0026rsquo;s vegan package (R Core Team 2022). The value of this index ranges between zero and one, where one indicates that all species are equally abundant.\u003c/p\u003e \u003cp\u003eTo evaluate the differences in species richness, total abundance of bird species, and abundance of generalist and specialists between the coffee plots and secondary forest plots, it was first necessary to rule out the possible system effect of these variables. System effects (for coffee and secondary forest plots) were evaluated using Generalized Linear Models (GLM). Residuals were adjusted to different distributions for each case (S6). For the analysis, the factor levels considered were coffee and secondary forest plots while the abundance of bird species in each plot was used as the response variable. Abundance was selected as the response variable due to correlations between species richness, abundance, and diversity. In all the cases, the model with the lowest Akaike Information Criteria corrected for small sample sizes (Delta AICc\u0026thinsp;\u0026lt;\u0026thinsp;2) was selected as the best model (Anderson et al. 2008) and confirmed by means of a Likelihood Ratio test (LRT) comparing the best model against the null model. Analyses were carried out in R (version 4.2.2.; R Core Team 2022). We used the following packages: \u003cem\u003elme4\u003c/em\u003e to build GLMs (Bates et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), \u003cem\u003eMuMIn\u003c/em\u003e for model selection (Bartoń \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), \u003cem\u003elmtest\u003c/em\u003e to verify model fit comparing best model vs. null model using the likelihood ratio test (LRT), and \u003cem\u003eemeans\u003c/em\u003e for the post-hoc analysis, using pairwise Tukey tests (Okoye and Hosseini \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Due to a high level of overdispersion, the model was unable to be fitted. As a solution, we used system as a random effect to avoid overdispersion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eGeneralist and specialist species in the assemblage\u003c/h2\u003e \u003cp\u003eOur research analysed bird species in coffee plantations and secondary forests, focusing on their feeding groups to understand their habitat preferences according to generalist or forest specialist species. To do so, we followed the methodology of Aben et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), which requires information on the natural history of each species. To compile natural history information for each species, we consulted various free natural history sources, including Birds of the World (2024), eBird, Neotropical Ornithological Society (NOS), BirdLife, and Xeno-Canto. Following review of the natural history information, the species were classified as forest generalists or forest specialists according to their affinity with the habitat (Aben et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Our study used generalized linear models and boxplots to evaluate whether there was a significant difference in the diversity, richness, or abundance of generalists and specialists between the two study systems. To graph this, we use box diagrams, where we include their comparison with the entire Nature's matrix.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eAssemblage and habitat conditions\u003c/h2\u003e \u003cp\u003eAt each circular plot, we measured the elevation, waterbodies (presence/absence), direction of slope, percentage of slope, plant richness, plant strata (trees, shrubs, young trees, herbs, and mosses) of tree leaf cover, shrub leaf cover, herbaceous leaf cover, and mosses leaf cover, in line with the methodology of Ralph et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), Su\u0026aacute;rez-Garc\u0026iacute;a et al. (2017), and Celis-Murillo et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). To evaluate the effects of habitat variables (independent variables) on species richness, as well as the abundance of generalists and specialists (response variables), we first explored the relation of each habitat variable and its interaction with each dependent variable. Then, we selected the independent variables and fitted different models. For the relationship of habitat variables, the numerical independent variables were scaled to standardize the data. As no differences were found for bird richness between systems, we put together the richness data from both systems (coffee and secondary forest plots). Next, we used a GLM to assess the relationship between bird richness and habitat variables, including a negative binomial distribution (Atkinson and Woods \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This was done to handle overdispersed count data. We considered system level as a random factor, because habitat variables were taken from different system levels (coffee plots and secondary forest) (Bolker \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBird assemblage composition\u003c/h2\u003e \u003cp\u003eA total of 1,010 individuals of 124 bird species were recorded, eight of which were migratory while the rest were resident birds. We found 68 bird species in secondary forest plots and 94 species in the coffee plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. a-c); 38 species were shared between coffee and secondary forest plots, of which 21 were specialists and 17 were generalists; 56 species were exclusive to the coffee plots, of which 24 were generalists and 32 were specialists; 30 species were exclusive to secondary forests, of which 15 were specialists and 15 were generalists.\u003c/p\u003e \u003cp\u003eThe rank-abundance curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. d) point to greater species richness in the coffee plots compared to the forest plots. Regarding the rank-abundance curves, each bird species is represented by a line on the graph that indicates its rank in terms of abundance and its relative abundance within the habitat. Judging by the differing width of the rank-abundance curves, species dominance between the coffee and secondary forest plots is not similar (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. d). The curves also indicate that the five most-common species differ in their distribution across the coffee and secondary forest plots and the entirety of Nature\u0026rsquo;s matrix. The extension of the curve line for secondary forest plots suggests slightly more species stability compared to coffee plots, but greater stability is observed throughout Nature\u0026rsquo;s matrix. Steep slopes on the rank-abundance curves indicate unequal distribution of species abundance. A pronounced numerical dominance of \u003cem\u003ePsarocolius decumanus\u003c/em\u003e (a generalist species) is indicated for coffee plots while a dominance of \u003cem\u003eMyiodynastes maculatus\u003c/em\u003e (a specialist species) is seen for secondary forest plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. d). For Nature\u0026rsquo;s matrix (sum of both systems), it can be seen how each system contributes different dominant species to the full assemblage. \u003cem\u003ePsarocolius decumanus\u003c/em\u003e (a generalist species) displays the highest relative abundance and appears dominant in coffee plots; followed by \u003cem\u003eMyiodynastes maculatus\u003c/em\u003e (a specialist species) that appears dominant in secondary forest plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. d). The dominant generalist and specialist species can be seen in S1\u0026ndash;S3.\u003c/p\u003e \u003cp\u003eOf the five most dominant species in coffee and secondary forest plots and throughout Nature\u0026rsquo;s matrix, the dominant frugivorous and omnivorous species observed were \u003cem\u003ePsarocolius decumanus\u003c/em\u003e and \u003cem\u003ePionus menstruus\u003c/em\u003e and \u003cem\u003eRamphastos vitellinus\u003c/em\u003e, followed by a granivorous bird, \u003cem\u003ePatagioenas plumbea\u003c/em\u003e, and an insectivore, \u003cem\u003eMyiodynastes maculatus.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe results of analysis of the similarity index point to low species similarity between coffee and secondary forest plots. We found only 0.35 (35%) similarity of all species between the coffee and secondary forest plots, as well as 0.3 (30%) similarity of generalist species and 0.26 (26%) similarity of specialist species between both systems. These results suggest that the assemblages of coffee and secondary forest plots do not feature similar species composition.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDiversity, richness of assemblages\u003c/h2\u003e \u003cp\u003eAccording to our analysis, coffee plots and secondary forest plots did not differ in terms of overall bird diversity (LRT: \u003cem\u003eχ\u0026sup2;\u003c/em\u003e = 0.048, d.f. = 1, P\u0026thinsp;=\u0026thinsp;0.825, ΔAIC\u0026thinsp;=\u0026thinsp;2.26; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-d, S4), abundance (LRT: \u003cem\u003eχ\u0026sup2;\u003c/em\u003e = 0.7497, d.f. = 1, P\u0026thinsp;=\u0026thinsp;0.386, ΔAIC\u0026thinsp;=\u0026thinsp;1.56; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. a-d, S4), Pielou evenness (LRT: \u003cem\u003eχ\u0026sup2;\u003c/em\u003e = 0.17, d.f. = 1, P\u0026thinsp;=\u0026thinsp;0.679, ΔAIC\u0026thinsp;=\u0026thinsp;2.14; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. a-d, S4), and richness (LRT: \u003cem\u003eχ\u0026sup2;\u003c/em\u003e = 0.002, d.f. = 1, P\u0026thinsp;=\u0026thinsp;0.965, ΔAIC\u0026thinsp;=\u0026thinsp;2.31; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. a-d, S4). However, when we distinguish between generalist and specialist species, our analysis shows that secondary forest plots bore higher specialist species richness than coffee plots, whereas the assemblage of generalist species was richer in coffee plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.)\u003c/p\u003e \u003cp\u003eSecondary forest plots featured lower variation in species richness and abundance for generalist species compared to coffee plots. However, secondary forest plots displayed significantly higher values for specialist species compared to coffee plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and S5 and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \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\u003eBest models\u0026rsquo; parameter estimates for the variables that showed variation explained by the system level (see S5) for generalist and specialist species.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ez value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep \u0026ndash; value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneralists\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRichness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Intercept: Coffee plot)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\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\u003eAbundance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Intercept: Coffee plot)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.9557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecialists\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRichness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Intercept: Coffee plot)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\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\u003eDiversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Intercept: Coffee plot)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.44 (\u003cem\u003et\u003c/em\u003e-value)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003cp\u003e(\u003cem\u003et-\u003c/em\u003e value)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAssemblages and habitat conditions\u003c/h2\u003e \u003cp\u003eOnly herbaceous leaf cover displayed a significant negative effect on all bird species richness, representing the entire assemblage of birds in Nature\u0026rsquo;s matrix (LRT: \u003cem\u003eχ\u0026sup2;\u003c/em\u003e = 4.754, d.f. = 1, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ΔAIC\u0026thinsp;=\u0026thinsp;2.45; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e., S7, and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We chose this variable because all the assemblage variables (richness, diversity, abundance) were correlated. We found that herbaceous leaf cover, leaf cover of shrubs, and plant strata explained the variation of abundance of generalists across Nature\u0026rsquo;s matrix (LRT: \u003cem\u003eχ\u0026sup2;\u003c/em\u003e = 66.05, d.f. = 1, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ΔAIC\u0026thinsp;=\u0026thinsp;58.76; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e., S7 and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Shrub leaf cover displayed significant positive effects on the abundance of generalists. The number of herbaceous leaf cover and plant strata was negatively related to the abundance of generalists (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e., S7, and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). None of the environmental variables explained variation in the abundance of specialists (LRT: \u003cem\u003eχ\u0026sup2;\u003c/em\u003e = 1.901, d.f. = 1, P\u0026thinsp;=\u0026thinsp;167, ΔAIC\u0026thinsp;=\u0026thinsp;0.52, S7 and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\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\u003eGLMM estimates averaged from the best models of abundance of generalists with delta AICc lower than two (see S8)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ez value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePr(\u0026gt;|z|)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBird species richness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003eHerbaceous leaf cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\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\u003eWaterbody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.643\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\u003ePlant richness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.797\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\u003ePlant strata\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbundance of generalists\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003ePlant strata\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003eHerbaceous leaf cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\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\u003eShrub leaf cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbundance of specialists\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.591\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\u003eWaterbody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.801\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\u003eHerbaceous leaf cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.835\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\u003eShrub leaf cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.74\u003c/p\u003e \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 research found that small-scale, forest-adjacent coffee plantations managed by traditional producers can host a diversity of bird assemblages comparable to or even greater than that of surrounding natural forests, refuting the hypothesis that coffee plantations always have lower diversity and richness of birds (Karp et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Indeed, our analysis found even more bird species in coffee plantations than in secondary forests (Faminow and Ariza \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Jones et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Leyequien et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Ocampo-Ariza et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Overall, landscape characteristics such as the amount of forest cover and vegetation heterogeneity positively influence the diversity and composition of bird communities (Alexandrino et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Perfecto et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Marconi and Armengot \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Specifically, we identified greater numbers of generalist and specialist species in the assemblages of coffee plantations compared to those of forests alone. This likely results from the special configuration of Nature\u0026rsquo;s matrix, featuring diverse habitats benefitting both bird groups, to which the small-scale coffee plantations contribute (Alexandrino et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ocampo-Ariza et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mart\u0026iacute;nez-Penados et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eVariation of bird assemblages between coffee plots and secondary forests in specialty coffee production systems\u003c/h2\u003e \u003cp\u003eOur research refutes the hypothesis that coffee-plot bird assemblages necessarily feature less diversity and richness than secondary forest plots (Karp et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). No significant difference was found in the diversity, richness, or abundance of bird species between the two systems. Indeed, more bird species were quantified in the coffee-plot assemblages compared to those of the secondary forest plots. According to Faminow and Ariza (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e); Jones et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2002\u003c/span\u003e); Leyequien et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2006\u003c/span\u003e); Perfecto et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); Ocampo-Ariza et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), coffee systems such as those we researched \u0026ndash; i.e. configured on a small-scale by traditional producers and surrounded by forest \u0026ndash; display bird diversity comparable to or greater than natural forests.\u003c/p\u003e \u003cp\u003eIn addition, according to Perfecto et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), landscapes like those in the present study feature greater biodiversity than landscapes dominated by large coffee plantations. We identified 94 bird species in our coffee plots and 68 in secondary forests. In a separate study, Ong\u0026rsquo;ondo et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) recorded 127 bird species in shade coffee plantations and 79 in forests. These findings support the hypothesis that coffee small-scale plantations can host more bird species than forests alone, even if they are monocultures or agroforestry systems like those in our study, in particular when they are configured near to forests.\u003c/p\u003e \u003cp\u003eFurther, our analysis revealed a similarity index of 35% when comparing the species composition of coffee-plot and forest-plot bird assemblages, not unlike the findings (similarity index 42%) of Ong\u0026rsquo;ondo et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This low similarity value indicates that coffee plantations cannot substitute for natural forests, but can rather serve as complementary sites on behalf bird conservation. In particular, our results confirm this in terms of dominant and exclusive species found in each system.\u003c/p\u003e \u003cp\u003eSpecifically, we found that \u003cem\u003eMyiodynastes maculatus\u003c/em\u003e and \u003cem\u003eRamphastos vitellinus\u003c/em\u003e (specialist birds) were dominant in secondary forest plots, whereas \u003cem\u003ePsarocolius decumanus\u003c/em\u003e and \u003cem\u003ePionus menstruus\u003c/em\u003e (generalist birds) were dominant in coffee plots. We found 30 species exclusive to secondary forest plots and 56 exclusive to coffee plots. These findings are also in line with those of Ong'ondo et al. (2022), who identified 18 bird species exclusive to forests and 66 exclusive to coffee systems. According to de Souza Leite et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), landscape characteristics explain variations in the abundance and presence/absence of birds identified in such environments. According to Harvey and Gonz\u0026aacute;lez Villalobos (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), differences in habitat and vegetation generate filters that favour certain birds, giving rise to dissimilar communities between natural habitats and agroecosystems such as those observed in our study. The Nature\u0026rsquo;s matrix created by the complementary plots in our study enables maintenance of exclusive assemblages of species according to morphological attributes (Ong\u0026rsquo;ondo et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn terms of generalist and specialist species, our results refute the hypothesis that forest assemblages feature more specialist species while coffee plantations feature more generalist species. Indeed, we found higher numbers of both groups in coffee plots. According to Alexandrino et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); Ocampo-Ariza et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), landscape-scale forest cover positively influences bird communities, especially forest and fruit-eating bird species like those in our study. The small-scale coffee plantations we studied were surrounded by forest cover, regardless of whether they were monocultures or agroforestry systems. According to Vel\u0026aacute;squez-Trujillo et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), a configuration like gives rise to greater species diversity than secondary forests.\u003c/p\u003e \u003cp\u003eIn terms of generalist species, we found significant differences in richness and abundance between the systems analysed. Specifically, we identified greater richness and abundance of generalists in the coffee plots compared to the secondary forest plots. However, no significant difference was found in terms of the diversity of generalists. These findings may be attributed to the landscape configuration. According to Muhamad et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Alexandrino et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), nearby forests and scattered trees in agricultural landscapes can act as corridors and \u0026ldquo;islands\u0026rdquo; that facilitate the movement and presence of bird species, including generalists and insectivores. In the agroforestry systems we studied, Torrico et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found more than 85 different plant species as shade trees. This suggests that the landscape configuration of our systems could explain the differences observed with regard to generalist bird species.\u003c/p\u003e \u003cp\u003eIn addition, we found significant differences regarding the richness and diversity of specialist bird species between coffee plots and secondary forest plots. In particular, we found greater richness and diversity of specialist bird species in the secondary forest plots. However, contrary to our hypothesis, we observed a higher number of pecialists in coffee plots compared to secondary forest plots. These patterns of diversity and richness of specialists could relate to the amount of forest cover present at the landscape scale, which was the same in the two systems under study (Alexandrino et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In our Nature\u0026rsquo;s matrix and that found elsewhere, forest cover positively influences the diversity and composition of bird communities (Robertson et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Further, the heterogeneity of the landscape in contexts such as ours provides good conditions for specialist species, in line with Alexandrino et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Tavares et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to our results, the configuration of the wider landscape \u0026ndash; particularly the amount of forest covers present and the heterogeneity of the vegetation structure belonging to Nature\u0026rsquo;s matrix \u0026ndash; may modulate the effects of plot-level homogenization on the diversity and composition of bird communities. This is consistent with patterns identified by Alexandrino et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Vel\u0026aacute;squez-Trujillo et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Ocampo-Ariza et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) with regard to agricultural landscapes. According to Ocampo-Ariza et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), it is necessary to consider these dynamics at multiple scales in order to develop effective conservation strategies in the context of specialty coffee production systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEffect of habitat on assemblages\u003c/h2\u003e \u003cp\u003eThe richness of all bird species was significantly affected by the percentage of herbaceous leaf cover: the greater the herbaceous leaf cover, the lower the bird species richness. No significant effects were found with respect to percentages of shrub leaf cover, plant richness, plant strata, elevation, or waterbodies. This could be because the bird species in our study were common to a particular stratum, similar to the species in the study by Jones et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur results support this because plant strata and the percentage of herbaceous leaf cover have a negative effect on the abundance of generalists. By contrast, the percentage of shrub leaf cover has a positive effect on the abundance of generalists. Buechley et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) indicate that different types of shade management in coffee plantations can generate more diverse vegetation cover, including trees, grasses, and shrubs \u0026ndash; that benefit bird species. Overall, according to Jones et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), shrubs are more common in forests and understory birds use coffee plantations as a conduit to travel between shrubs in search of food. Therefore, the bird species in our study, especially the generalists, are likely understory birds for whom shrub cover is particularly relevant.\u003c/p\u003e \u003cp\u003eIn line with Bhagwat et al. (2008), this indicates that the amount of forest cover present at the landscape scale is key to the diversity and composition of bird communities. Shrub cover in forests and coffee plantations benefits understory birds, as observed by Jones et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and Buechley et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These bird species can use it as runners in search of food (Jones et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Alexandrino et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe variables of elevation, plant richness, and waterbodies had no significant effect on the abundance of generalists. While these elements belong to Nature\u0026rsquo;s matrix Perfecto et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), they do not appear to make a direct contribution to bird diversity in the context of specialty coffee landscapes. According to de Souza Leite et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), landscape-scale factors are more important than plot- or patch-level factors in determining bird diversity patterns.\u003c/p\u003e \u003cp\u003eNo habitat variables affected specialist abundance in the present study. This indicates that our Nature\u0026rsquo;s matrix was high quality and featured less contrast between habitats, precluding edge effects like predation and parasitism that particularly impact specialists (Leyequien et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). According to de Souza Leite et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), natural corridors that enable birds to access forests are particularly important, as habitat specialist species depend on forest environments for their reproduction and survival.\u003c/p\u003e \u003cp\u003eOverall, our findings indicate that the landscape configuration of Nature\u0026rsquo;s matrix \u0026ndash; including forest cover, habitat heterogeneity, and connecting elements \u0026ndash; plays a key role in modulating habitat effects on biodiversity (Olson and Dinerstein \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Olden and Rooney \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Garc\u0026iacute;a and Mart\u0026iacute;nez \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In this way, maintaining a variety of habitats within Nature\u0026rsquo;s matrix \u0026ndash; including coffee plantations featuring diverse cover (Buechley et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and surrounded by forests (de Souza Leite et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) \u0026ndash; can benefit both generalist and specialist bird species (Mart\u0026iacute;nez-Penados et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this research, no variation was found in bird communities between coffee plots and secondary forests in specialty coffee production systems.. Our study found that small-scale coffee plantations (monocultures and agroforestry systems) can host levels of bird diversity comparable to or even greater than that of natural forests, refuting the hypothesis that coffee plantations necessarily have lower bird diversity and richness. This appears related to special characteristics of the landscape. Contrary to our predictions, a higher number of generalist and specialist species were found in coffee plantations compared to forests. This may be attributed to the configuration of Nature\u0026rsquo;s matrix, in particular when it encompasses diverse habitats beneficial to both specialist and generalist bird species.\u003c/p\u003e \u003cp\u003eWe conclude that specialty coffee plots can provide habitats that complement those of neighbouring forests in conserving bird species. As evidenced by our study, dispersed coffee plots surrounded by forest cover can host species compositions similar to those found in secondary forests.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received funding from the project \u0026ldquo;Exploring the interlinkages between specialty coffee farms biodiversity and farmer\u0026rsquo;s practices in Bolivia\u0026rdquo; Seed Money Grant 1912, with the financial support of the Leading House for the Latin American Region, Latin American Swiss Center (CLS-HSG), University of St. Gallen, and the State Secretariat for Education, Research and Innovation (SERI), Switzerland.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the main text of the manuscript, but NA had the greatest involvement, NA prepared Figures 1 to 5. All authors reviewed the manuscript. GT contributed with forums for the study area. VT contributed to the map of the study area.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank the coffee farmers of Caranavi for allowing us access to their farms. Special thanks also go to D. Mendez for lending us the recording equipment, to D. Camacho for helping us with some bird identifications, and to our field guides: S.R. L\u0026oacute;pez-Guti\u0026eacute;rrez, S. Past\u0026eacute;n, and S.V. Condori-Coarite. The research was funded by a seed grant from the Swiss Secretariat for Education, Research, and Innovation (SERI).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eLos datos de diversidad riqueza y abundancia respaldan los hallazgos de este estudio junto con los datos de abundancia de especies generalistas y especialistas. Los datos se proporcionan dentro del manuscrito y en los archivos de informaci\u0026oacute;n complementaria.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAben J, Dorenbosch M, Herzog SK, et al (2008) Human Disturbance affects a Deciduous Forest Bird Community in the Andean Foothills of Central Bolivia. Bird Conserv Int 18:363\u0026ndash;380. https://doi.org/10.1017/S0959270908007326\u003c/li\u003e\n\u003cli\u003eAlexandrino ER, Buechley ER, Karr JR, et al (2017) Bird based Index of Biotic Integrity: Assessing the ecological condition of Atlantic Forest patches in human-modified landscape. Ecol Indic 73:662\u0026ndash;675. https://doi.org/10.1016/j.ecolind.2016.10.023\u003c/li\u003e\n\u003cli\u003eAlvarez-Alvarez EA, Almaz\u0026aacute;n-N\u0026uacute;\u0026ntilde;ez RC, Corcuera P, et al (2022) Land use cover changes the bird distribution and functional groups at the local and landscape level in a Mexican shaded-coffee agroforestry system. 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Accessed 18 Jul 2024e\u003c/li\u003e\n\u003c/ol\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":"Bird assemblages, Nature’s matrix, specialty coffee, sustainability","lastPublishedDoi":"10.21203/rs.3.rs-4825928/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4825928/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoffee-related agricultural intensification affects bird species abundance, richness, and composition through habitat loss and degradation. Production of specialty coffee is expected to be more sustainable and environmentally friendly than conventional coffee. Nevertheless, not all specialty coffee is grown sustainably. To evaluate environmental sustainability, we evaluated the composition of bird assemblages in six specialty coffee-producing communities in Bolivia\u0026rsquo;s pre-montane subtropical humid forest region. To do this, we measured the diversity, richness, generalist and specialist species, and the effect of habitat on bird assemblages, comparing coffee plots and secondary forest plots as part of \u0026ldquo;Nature\u0026rsquo;s matrix\u0026rdquo;. We found significant differences in the abundance of generalist bird species. We did not find differences in the richness and diversity of specialist species. Plant strata, herbaceous leaf cover, and shrub leaf cover affected the assemblages of generalist species. Our results represent a first step toward understanding the intricate relationship between biodiversity and specialty coffee production, highlighting the importance of considering regional differences in landscape characteristics \u0026ndash; conceived of as Nature\u0026rsquo;s matrix \u0026ndash; when examining biodiversity in specialty coffee systems.\u003c/p\u003e","manuscriptTitle":"Bird assemblages in specialty coffee production landscapes in pre-montane humid subtropical forests","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-28 08:04:02","doi":"10.21203/rs.3.rs-4825928/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":"754ba7ac-31d1-4167-9fae-be99012bbe6b","owner":[],"postedDate":"August 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-04T10:38:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-28 08:04:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4825928","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4825928","identity":"rs-4825928","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}